A General Method for Estimating a Linear Structural Equation System
The substantially upgraded new version marks the golden jubilee of a seminal development in the history of Structure Equation Modeling (SEM). A little over a half century ago Professor Karl Jöreskog published a monograph in the Educational Testing Service (ETS) Research Bulletin series entitled A General Method for Estimating a Linear Structural Equation System, along with the LISREL software program.
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This paper gives a rather general review of the L1 norm algorithms. The chronology and historical development of the L1 norm estimation theory for the period of 1632-1928 will be surveyed and the algorithms belonging to the after 1928 period will be categorized into three main classes of direct descent, simplex type, and other algorithms.
An alternative scheme for approximating a periodic functionijscmcj
Fourier series is generally used in applied mathematics and non-linear mechanics to solve the problems containing periodic functions. The aim of this paper is to present a new scheme through involving the Taylor’s series after some process modification for all such problems. It will save the long evaluation process involve in computing the different constants of Fourier series. The result obtained by the present
method has been compared with the result from the Fourier series expansion w.r.t. the exact solution and
found to be more satisfactory.
This paper gives a rather general review of the L1 norm algorithms. The chronology and historical development of the L1 norm estimation theory for the period of 1632-1928 will be surveyed and the algorithms belonging to the after 1928 period will be categorized into three main classes of direct descent, simplex type, and other algorithms.
An alternative scheme for approximating a periodic functionijscmcj
Fourier series is generally used in applied mathematics and non-linear mechanics to solve the problems containing periodic functions. The aim of this paper is to present a new scheme through involving the Taylor’s series after some process modification for all such problems. It will save the long evaluation process involve in computing the different constants of Fourier series. The result obtained by the present
method has been compared with the result from the Fourier series expansion w.r.t. the exact solution and
found to be more satisfactory.
Sparse Observability using LP Presolve and LTDL Factorization in IMPL (IMPL-S...Alkis Vazacopoulos
Presented in this short document is a description of our technology we call “Sparse Observability”. Observability is the estimatability metric (Bagajewicz, 2010) to structurally determine that an unmeasured variable or regressed parameter is either uniquely solvable (observable) or otherwise unsolvable (unobservable) in data reconciliation and regression (DRR) applications. Ultimately, our purpose to use efficient sparse matrix techniques is to solve large industrial DRR flowsheets quickly and accurately.
Most other implementations of observability calculation use dense linear algebra such as reduced row echelon form (RREF), Gauss-Jordan decomposition (Crowe et. al. 1983; Madron 1992), QR factorization which can now be considered as semi-sparse (Swartz, 1989; Sanchez and Romagnoli, 1996), Schur complements, Cholesky factorization (Kelly, 1998a) and singular value decomposition (SVD) (Kelly, 1999). A sparse LU decomposition with complete-pivoting from Albuquerque and Biegler (1996) for dynamic data reconciliation observability computation was used but it is uncertain if complete-pivoting causes extreme “fill-ins” of the lower and upper triangular matrices essentially making them near-dense. There is another sparse observability method using an LP sub-solver found in Kelly and Zyngier (2008) but this requires solving as many LP sub-problems as there are unmeasured variables which can be considered as somewhat inefficient.
IMPL’s sparse observability technique uses the variable classification and nomenclature found in Kelly (1998b) given that if we partition or separate the unmeasured variables into independent (B12) and dependent (B34) sub-matrices then all dependent unmeasured variables by definition are unobservable. If any independent unmeasured variable is a (linear) function of any dependent variable then this independent variable is of course also unobservable because it is dependent on another non-observable variable.
A Regularization Approach to the Reconciliation of Constrained Data SetsAlkis Vazacopoulos
A new iterative solution to the statistical adjustment of constrained data sets is derived in this paper. The method is general and may be applied to any weighted least squares problem containing nonlinear equality constraints. Other methods are available to solve this class of problem, but are complicated when unmeasured variables and model parameters are not all observable and the model constraints are not all independent. Of notable exception however are the methods of Crowe (1986) and Pai and Fisher (1988), although these implementations require the determination of a matrix projection at each iteration which may be computationally expensive. An alternative solution is proposed which makes the pragmatic assumption that the unmeasured variables and model parameters are known with a finite but equal uncertainty. We then re-formulate the well known data reconciliation solution in the absence of these unknowns to arrive at our new solution; hence the regularization approach. Another procedure for the classification of observable and redundant variables is also given which does not require the explicit computation of the matrix projection. The new algorithm is demonstrated using three illustrative examples previously used in other studies.
IOSR Journal of Mathematics(IOSR-JM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
ON THE CATEGORY OF ORDERED TOPOLOGICAL MODULES OPTIMIZATION AND LAGRANGE’S PR...IJESM JOURNAL
A category is an algebraic structure made up of a collection of objects linked together by morphisms. As a foundation of mathematics, categories were created as a way of relating algebraic structures and systems of topological spaces In this paper we define a derivative using cones in the category of topological modules and use the Lagrange’s principle to obtain optimization results in the category.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Sparse Observability using LP Presolve and LTDL Factorization in IMPL (IMPL-S...Alkis Vazacopoulos
Presented in this short document is a description of our technology we call “Sparse Observability”. Observability is the estimatability metric (Bagajewicz, 2010) to structurally determine that an unmeasured variable or regressed parameter is either uniquely solvable (observable) or otherwise unsolvable (unobservable) in data reconciliation and regression (DRR) applications. Ultimately, our purpose to use efficient sparse matrix techniques is to solve large industrial DRR flowsheets quickly and accurately.
Most other implementations of observability calculation use dense linear algebra such as reduced row echelon form (RREF), Gauss-Jordan decomposition (Crowe et. al. 1983; Madron 1992), QR factorization which can now be considered as semi-sparse (Swartz, 1989; Sanchez and Romagnoli, 1996), Schur complements, Cholesky factorization (Kelly, 1998a) and singular value decomposition (SVD) (Kelly, 1999). A sparse LU decomposition with complete-pivoting from Albuquerque and Biegler (1996) for dynamic data reconciliation observability computation was used but it is uncertain if complete-pivoting causes extreme “fill-ins” of the lower and upper triangular matrices essentially making them near-dense. There is another sparse observability method using an LP sub-solver found in Kelly and Zyngier (2008) but this requires solving as many LP sub-problems as there are unmeasured variables which can be considered as somewhat inefficient.
IMPL’s sparse observability technique uses the variable classification and nomenclature found in Kelly (1998b) given that if we partition or separate the unmeasured variables into independent (B12) and dependent (B34) sub-matrices then all dependent unmeasured variables by definition are unobservable. If any independent unmeasured variable is a (linear) function of any dependent variable then this independent variable is of course also unobservable because it is dependent on another non-observable variable.
A Regularization Approach to the Reconciliation of Constrained Data SetsAlkis Vazacopoulos
A new iterative solution to the statistical adjustment of constrained data sets is derived in this paper. The method is general and may be applied to any weighted least squares problem containing nonlinear equality constraints. Other methods are available to solve this class of problem, but are complicated when unmeasured variables and model parameters are not all observable and the model constraints are not all independent. Of notable exception however are the methods of Crowe (1986) and Pai and Fisher (1988), although these implementations require the determination of a matrix projection at each iteration which may be computationally expensive. An alternative solution is proposed which makes the pragmatic assumption that the unmeasured variables and model parameters are known with a finite but equal uncertainty. We then re-formulate the well known data reconciliation solution in the absence of these unknowns to arrive at our new solution; hence the regularization approach. Another procedure for the classification of observable and redundant variables is also given which does not require the explicit computation of the matrix projection. The new algorithm is demonstrated using three illustrative examples previously used in other studies.
IOSR Journal of Mathematics(IOSR-JM) is an open access international journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
ON THE CATEGORY OF ORDERED TOPOLOGICAL MODULES OPTIMIZATION AND LAGRANGE’S PR...IJESM JOURNAL
A category is an algebraic structure made up of a collection of objects linked together by morphisms. As a foundation of mathematics, categories were created as a way of relating algebraic structures and systems of topological spaces In this paper we define a derivative using cones in the category of topological modules and use the Lagrange’s principle to obtain optimization results in the category.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Ridge regression method is an improved method when the assumptions of independence of the explanatory variables cannot be achieved, which is also called multicollinearity problem, in regression analysis. One of the way to eliminate the multicollinearity problem is to ignore the unbiased property of . Ridge regression estimates the regression coefficients biased in order to decrease the variance of the regression coefficients.
Statistica Sinica 16(2006), 847-860
PSEUDO-R
2
IN LOGISTIC REGRESSION MODEL
Bo Hu, Jun Shao and Mari Palta
University of Wisconsin-Madison
Abstract: Logistic regression with binary and multinomial outcomes is commonly
used, and researchers have long searched for an interpretable measure of the strength
of a particular logistic model. This article describes the large sample properties
of some pseudo-R2 statistics for assessing the predictive strength of the logistic
regression model. We present theoretical results regarding the convergence and
asymptotic normality of pseudo-R2s. Simulation results and an example are also
presented. The behavior of the pseudo-R2s is investigated numerically across a
range of conditions to aid in practical interpretation.
Key words and phrases: Entropy, logistic regression, pseudo-R2
1. Introduction
Logistic regression for binary and multinomial outcomes is commonly used
in health research. Researchers often desire a statistic ranging from zero to one
to summarize the overall strength of a given model, with zero indicating a model
with no predictive value and one indicating a perfect fit. The coefficient of deter-
mination R2 for the linear regression model serves as a standard for such measures
(Draper and Smith (1998)). Statisticians have searched for a corresponding in-
dicator for models with binary/multinomial outcome. Many different R2 statis-
tics have been proposed in the past three decades (see, e.g., McFadden (1973),
McKelvey and Zavoina (1975), Maddala (1983), Agresti (1986), Nagelkerke
(1991), Cox and Wermuch (1992), Ash and Shwartz (1999), Zheng and Agresti
(2000)). These statistics, which are usually identical to the standard R2 when
applied to a linear model, generally fall into categories of entropy-based and
variance-based (Mittlböck and Schemper (1996)). Entropy-based R2 statistics,
also called pseudo-R2s, have gained some popularity in the social sciences (Mad-
dala (1983), Laitila (1993) and Long (1997)). McKelvey and Zavoina (1975)
proposed a pseudo-R2 based on a latent model structure, where the binary/
multinomial outcome results from discretizing a continuous latent variable that
is related to the predictors through a linear model. Their pseudo-R2 is defined
as the proportion of the variance of the latent variable that is explained by the
848 BO HU, JUN SHAO AND MARI PALTA
covariate. McFadden (1973) suggested an alternative, known as “likelihood-
ratio index”, comparing a model without any predictor to a model including all
predictors. It is defined as one minus the ratio of the log likelihood with inter-
cepts only, and the log likelihood with all predictors. If the slope parameters
are all 0, McFadden’s R2 is 0, but it is never 1. Maddala (1983) developed
another pseudo-R2 that can be applied to any model estimated by the maximum
likelihood method. This popular and widely used measure is expressed as
R2M = 1 −
(
L(θ̃)
L(θ̂)
)
2
n
, (1)
.
Statistica Sinica 16(2006), 847-860
PSEUDO-R
2
IN LOGISTIC REGRESSION MODEL
Bo Hu, Jun Shao and Mari Palta
University of Wisconsin-Madison
Abstract: Logistic regression with binary and multinomial outcomes is commonly
used, and researchers have long searched for an interpretable measure of the strength
of a particular logistic model. This article describes the large sample properties
of some pseudo-R2 statistics for assessing the predictive strength of the logistic
regression model. We present theoretical results regarding the convergence and
asymptotic normality of pseudo-R2s. Simulation results and an example are also
presented. The behavior of the pseudo-R2s is investigated numerically across a
range of conditions to aid in practical interpretation.
Key words and phrases: Entropy, logistic regression, pseudo-R2
1. Introduction
Logistic regression for binary and multinomial outcomes is commonly used
in health research. Researchers often desire a statistic ranging from zero to one
to summarize the overall strength of a given model, with zero indicating a model
with no predictive value and one indicating a perfect fit. The coefficient of deter-
mination R2 for the linear regression model serves as a standard for such measures
(Draper and Smith (1998)). Statisticians have searched for a corresponding in-
dicator for models with binary/multinomial outcome. Many different R2 statis-
tics have been proposed in the past three decades (see, e.g., McFadden (1973),
McKelvey and Zavoina (1975), Maddala (1983), Agresti (1986), Nagelkerke
(1991), Cox and Wermuch (1992), Ash and Shwartz (1999), Zheng and Agresti
(2000)). These statistics, which are usually identical to the standard R2 when
applied to a linear model, generally fall into categories of entropy-based and
variance-based (Mittlböck and Schemper (1996)). Entropy-based R2 statistics,
also called pseudo-R2s, have gained some popularity in the social sciences (Mad-
dala (1983), Laitila (1993) and Long (1997)). McKelvey and Zavoina (1975)
proposed a pseudo-R2 based on a latent model structure, where the binary/
multinomial outcome results from discretizing a continuous latent variable that
is related to the predictors through a linear model. Their pseudo-R2 is defined
as the proportion of the variance of the latent variable that is explained by the
848 BO HU, JUN SHAO AND MARI PALTA
covariate. McFadden (1973) suggested an alternative, known as “likelihood-
ratio index”, comparing a model without any predictor to a model including all
predictors. It is defined as one minus the ratio of the log likelihood with inter-
cepts only, and the log likelihood with all predictors. If the slope parameters
are all 0, McFadden’s R2 is 0, but it is never 1. Maddala (1983) developed
another pseudo-R2 that can be applied to any model estimated by the maximum
likelihood method. This popular and widely used measure is expressed as
R2M = 1 −
(
L(θ̃)
L(θ̂)
)
2
n
, (1)
.
Certain Algebraic Procedures for the Aperiodic Stability Analysis and Countin...Waqas Tariq
To evaluate the performance of a linear time-invariant system, various measures are available. In this paper employing Routh’s table, two geometrical criteria for the aperiodic stability analysis of linear time-invariant systems having real coefficients are formulated. The proposed algebraic stability criteria check whether the given linear system is aperiodically stable or not.The additional significance of the two criteria is , it can be used to count the number of complex roots of a system having real coefficients which is not possible by the use of original Routh’s Table. These procedures can also be used for the design of linear systems. In the proposed methods , the characteristic equation having real coefficients are first converted to complex coefficient equations using Romonov’s transformation. These complex coefficients are used in two different ways to form the Modified Routh’s tables for the two schemes named as Sign Pair Criterion I (SPC I) and Sign Pair Criterion II (SPC II). It is found that the proposed algorithms offer computational simplicity compared to other algebraic methods and is illustrated with suitable examples. The developed MATLAB program make the analysis most simple.
STATISTICAL ANALYSIS OF FUZZY LINEAR REGRESSION MODEL BASED ON DIFFERENT DIST...Wireilla
Using fuzzy linear regression model, the least squares estimation for linear regression (LR) fuzzy number is studied by Euclidean distance, Y-K distance and Dk distance respectively. It is concluded that the three different distances have the same coefficient of the least squares estimation. The data simulation shows the correctness of this conclusion.
STATISTICAL ANALYSIS OF FUZZY LINEAR REGRESSION MODEL BASED ON DIFFERENT DIST...ijfls
Using fuzzy linear regression model, the least squares estimation for linear regression (LR) fuzzy number is
studied by Euclidean distance, Y-K distance and Dk
distance respectively. It is concluded that the three
different distances have the same coefficient of the least squares estimation. The data simulation shows the
correctness of this conclusion.
TYPE-2 FUZZY LINEAR PROGRAMMING PROBLEMS WITH PERFECTLY NORMAL INTERVAL TYPE-...ijceronline
In this paper, the Perfectly normal Interval Type-2 Fuzzy Linear Programming (PnIT2FLP) model is considered. This model is reduced to crisp linear programming model. This transformation is performed by a proposed ranking method. Based on the proposed fuzzy ranking method and arithmetic operation, the solution of Perfectly normal Interval Type-2 Fuzzy Linear Programming model is obtained by the solutions of linear programming model with help of MATLAB. Finally, the method is illustrated by numerical examples.
This paper tries to compare more accurate and efficient L1 norm regression algorithms. Other comparative studies are mentioned, and their conclusions are discussed. Many experiments have been performed to evaluate the comparative efficiency and accuracy of the selected algorithms.
Versions and Latest Releases
Version 16: with the newest release of version 16d, we introduce a new input style, called Desirable Inputs Model. In this new model, we allow some input style (called IGood) which are larger the better. Examples include number of electric vehicles in an environmental model, the number of test takers in vaccine development model, etc. For more details, go to newsletter 20.
Focused Analysis of Qualitative Interviews with MAXQDA
Step by Step
Focused Analysis of Qualitative Interviews with MAXQDA
Authors: Stefan Rädiker, Udo Kuckartz
Pages: 125
Released: 2020
Language: English
ISBN: 978-3-948768072
DOI: 10.36192/978-3-948768072
All-in-One Website Security Scanner
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DEA-Solver-Pro Version 14d- Newsletter17
The latest release of version 14 is 14d, with a new feature SBM Bounded Model as an extention to SBM Max of version 13, which replaced SBM Variation model of version 12. In the real world, there are cases where input resources and/od output expansion are restricted by external constraints. SBM Bounded Model takes care of such situations, so that the outcome of SBM Bounded Model becomes more realistic than before. Note that these SBM models essentially represent KAIZEN improvement. For more details, go to newsletter 17.
NativeJ is a powerful Java EXE maker. The executable generated by NativeJ is uniquely customized to launch your Java application under Windows. NativeJ is not a compiler! Think of NativeJ-generated executables as supercharged "binary batch files"
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
Edraw Max - All-In-One Diagram Software
Edraw Max is a versatile diagram software, with features that make it perfect not only for professional-looking flowcharts, org charts, network diagrams and mind maps, but also building plans, business charts, workflows, fashion designs, UML diagrams, electrical engineering diagrams, directional maps and database model diagrams.
EdrawSoft Edraw Max 特別版是一整合圖示繪製軟體,新穎小巧,功能強大,可以很方便的繪製各種專業的流程圖、組織結構圖、網路拓撲圖、傢俱設計圖、商業圖表等。
應用領域:流程圖、網路拓撲圖、組織結構圖、工作流程圖、UML,軟體設計、商業圖表、2D, 3D 圖形、計畫 / 報表、地圖,方向圖、資料庫等。
購買及下載請聯絡
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Tel : 886-4-2386-3559 Fax : 886-4-2386-3159
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Atlas.ti 8 質性分析軟體新功能介紹!
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559
NEW VERSION OUT NOW! We are happy to announce that ATLAS.ti 8 is released now! Completely re-designed in nearly every aspect, ATLAS.ti 8 Windows is poised to set new standards for computer-assisted qualitative data analysis. What's new? Find out about the new powerfull features of ATLAS.ti 8 here http://bit.ly/2hDIK0H.
**ATLAS.ti licenses purchased after April 1, 2015 qualify for a FREE UPGRADE
New Features
These are some of the powerful new features:
Under the hood: Clean separation of data layer, application logic, and user interface, latest technology for safe and reliable performance.
Unicode throughout
Undo/Redo (100 steps)
Direct import of Twitter, Endnote, Evernote data
Powerful Visual Query Editor for creating and modifying SmartCodes and SmartGroups
Full project search (former “Word cruncher”) significantly improved with dynamic fade-in/fade-out hit categories
Elegant and trememdously useful new network layout options
Network groups
Memo comments
State-of-the-art, highly intuitive user interface with ribbons, tabbed views, flexible navigation areas.
All tool windows can be freely positioned
Multiple documents
More powerful “margin” than ever, many new interactive functions.
Features Yet To Come
At the time of the RC1 release, the following areas are still missing or incomplete:
Project exchange between ATLAS.ti Mac and ATLAS.ti Windows
Teamwork scenario with central, shared project directories
Non-English user interface
Some specific functionalities (see below)
Functionality still to be added:
Transcription
Document editing
Print documents with margin
Global filters
Interrater reliability
Relative values in code-doc table
XML converter
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559
Maxqda12 features -detailed feature comparison for more information about each product
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx
A General Method for Estimating a Linear Structural Equation System
1. A GENERAL METHOD FOR ESTIMATING A LINEAR STRUCTURAL
R
~
(
A
R
~ ~
T
I
N
EQUATION SYSTEM
II
Karl G. Joreskog
RB-70-54
This Bulletin is a draft for interoffice circulation.
Corrections and suggestions for revision are solicited.
The Bulletin should not be cited as a reference without
the specific permission of the author. It is automati-
cally superseded upon formal publication of the material.
Educational Testing Service
Princeton, New Jersey
September 1970
2. A General Method for Estimating a Linear Structural
Equation System
Karl G. J8reskog
Educational Testing Service
Abstract
A general method for estimating the unknown coefficients in a set
of linear structural equations is described. In its most general form the
method allows for both errors in equations (residuals, disturbances) and
errors in variables (errors of measurement, observational errors) and yields
estimates of the residual variance-covariance matrix and the measurement error
variances as well as estimates of the unknown coefficients in the structural
equations, provided all these parameters are identified. Two special
cases of this general method are discussed separately. One is when there
are errors in equations but no errors in variables. The other is when there
are errors in variables but no errors in equations. The methods are applied
and illustrated using artificial, economic and psychological data.
3. A General Method for Estimating a Linear Structural
Equation System*
1. Introduction
We shall describe a general method for estimating the unknown coe~ficients
in a set of linear structural equations. In its most general form the method
will allow for both errors in equations (residuals, disturbances) and errors
in variables (errors of measurement, observational errors) and will yield
estimates of the residual variance-covariance matrix and the measurement error
variances as well as estimates of the unknown coefficients in the structural
equations, provided all .these parameters are identified. After giving
the results for this general case, two special cases will be considered.
The first is the case when there are errors in equations but no errors in
variables. This case has been studied extensively by econometricians (see
e.g., Goldberger, 1964, Chapter 7). The second case is when there are
errors in variables but no errors in equations. Models of this kind have
been studied under the name of path analysis by biometricians (see e.g.,
Turner & Stevens, 1959») sociologists (see e.g., Blalock, 1964) and psycholo-
gists (Werts & Linn, 1970).
It is assumed that the observed variables have a multinormal distribu-
tion and the unknmYn parameters ar~ estimated by the maximum likelihood method.
The estimates are computed numerically using a modi~ication of the Fletcher-
Powell minimization algorithm (Fletcher & Powell, 1963; Gruvaeus &JBreskog,
1970). Standard errors of the estimated parameters may be obtained by
computing the inverse of the information matrix. A computer program,
*This research has been supported in part by grant NSF-GB-12959 from the
National Science Foundation. The author wishes to thank Professor Arthur
Goldberger for his . comments on an earlier draft of the paper and Marielle
van Thillo, who wrote the computer programs, checked the mathematical
derivations and gave other valuable assistance throughout the work.
4. -2-
LISREL, in FORTRAN IV, that performs all the necessary computations has been
written and tested out on the IBM 360/65; a write-up of this is under
preparation (J8reskog & van Thillo, 1970).
In the first special case referred to above, where there are no errors
of measurement in the observed variables the general method to be presented
is equivalent to the full information maximum likelihood (FIML) method of
Koopmans, Rubin and Leipnik (1950) also called full information least
generalized residual variance (FILGRV) method (Goldberger, 1964, Chapter 7),
provided that no constraints are imposed on the residual variance-covariance
matrix and the variance-covariance matrix of the independent variables.
However, with the general method described here, it is possible to assign
fixed values to some elements of these matrices and also to have equality
constraints among the remaining elements.
2. The General Model
Consider random vectors ~t ~ (~1'~2' •• ·'~) and S' ~ (Sl'S2' ••• 'Sn)
of true dependent and independent variables, respectively, and the following
system of linear structural relations
(1)
where B(m x m) and ~(m x n) are coefficient matrices and ~I ~ (~l' ~, ••• ,
Sm) is a random vector of residuals (errors in equations, random disturbance
terms). Without loss of generality it may be assumed that e(~) = e(~)~ Q
and e(~) ~ o. It is furthermore assumed that S is uncorrelated with
s and that ~ is nonsingular.
5. -3-
The vectors ~ and e are not observed but instead vectors yl =
(Yl'Y2'·"'Ym) and Xl = (xl,x2, ... ,x
n)
are observed, such that
Y (2)
x=v+~+5
where I-l = ,e(y) v = e(~) and E and 5 are vectors of errors of measure-
ment in y and ~, respectively. It is convenient to refer to y and x
as the observed variables and ~ and s as the true variables. The errors
of measurement are assumed to be uncorrelated with the true variates and
among themselves.
Let p(n x n) and
and s} respectively,
be the variance-covariance matrices of
8
2
the diagonal matrices of error
_5
variances for y and ~, respectively. Then it follows, from the above
assumptions, that the variance-covariance matrix E[(m + n) x (m + n)] of
z = (y"::5 r )' is
(4)
~-l~~1~1-l
E =f -
~::.~.-l
The elements of E are functions of the elements of B} r, ¢, ~,
and 8 .
~E
In applications some of these elements are fixed and equal
to assigned values. In particular this is so for elements in B
- and I' '
but we shall allow for fixed values even in 'the other matrices. For the
remaining nonfixed elements of the six parameter matrices one or more subsets
may have identical but unknown values. Thus parameters in B, E', p,
0/ , and s
~E
are of three kinds: (i) fixed parameters that have been
6. -4-
assigned given values, (ii) constrained parameters that are unknown but equal
to one or more other parameters and (iii) free parameters that are unknown and
not constrained to be equal to any other parameter.
Before an attempt is made to estimate a model of this kind, the identi-
fication problem must be examined. The identification problem depends on
the specification of fixed, constrained and free parameters. Under a given
specification, a given structure ~' r} ~, t, ~5} (3 generates
-€
one and only one ~ but there may be several structures generating the
same ~. If two or more structures generate the same ~, the structures
are said to be equivalent. If a parameter has the same value in all equiva-
lent structure~ the parameter is said to be identified. If all parameters
of the model are identified, the whole model is said to be identified. When
a model is identified one can usually find consistent estimates of all its
parameters. Some rules for investigating the identification problem when
there are no errors in variables are given by Goldberger (1964, pp. 306-318).
3. Estimation of the General Model
Let ~1'~2' ···}~N be N observations of z = (~t}~t)J • Since no
constraints are imposed on the mean vector (~t,yr)r the maximum likelihood
ti th · · t t z = (y-', x- t ) t
es mate of 1S 1S he usual sample mean vec or •
1 N
§ = - ~ (z~ - ~)(~a - ~),
N a=l "'U
be the usual sample variance-covariance matrix, partitioned as
Let
~[(m + n) x (m + n)]
=[~yy(m x m)
S (n x m)
~xy
S (m x
~yx
S (n x
-xx
n)j
n)
(6)
7. -5-
The logarithm of the likelihood function, omitting a function of the
observations, is given by
This is regarded as a function of the independent distinct parameters
in B, r , ~, IF, e and e and is to be maximized with respect
-0 -e
to these, taking into account that some elements may be fixed and some
may be constrained to be equal to some others. Maximizing log L is equiva-
lent to minimizing
(8)
Such a minimization problem may be formalized as follows.
Let At ~ (~,~, ••• ,Ap) be a vector of all the elements of ~, r
~, IF, e and 8 arranged in a prescribed order. Then F may be
.- _0 -E
regarded as a function
continuous derivatives
F(~)
of/OAs
of ~,A2""'"
and o~/Oo
which is continuous and has
of first and second order,
except where ~ is singular. The totality of these derivatives is repre-
sented by a gradient vector OF/O~ and a symmetric matrix o2F/Of}J~t • Now
let some p - q of the A's be fixed and denote the remaining A's by ~l'
~2""'~q' q ~ p. The function F is now considered as a function G(~)
of ~1'~2""'~q' Derivatives OG/O~ and 02G/d~d~' are obtained from
OF/d?:: and o2F/o?jJ?
Y by omitting rows and columns corresponding to the fixed
A's. Among ~1'~2' ""~q , let there be some r distinct parameters denoted
~1'~2,···,Kr' r ~ q , so that each ~i is equal to one and only one K
j
,
8. -6-
but possibly several :refS equal the same ~. Let
order q x r with elements k
i j = 1 if :rei = K
j
The function F (or G) is now a function H(~)
have
K =
and
of
(k .. ) be a matrix of
lJ
k .. = 0 otherwise.
lJ
and we
(10)
Thus, the derivatives of H are simple sums of the derivatives of· G •
The minimization of H(~) is now a straightforward application of
the Fletcher-Powell method for which a computer program is available
(Gruvaeus & J8reskog, 1970). This method makes use of a matrix ~, which
is evaluated in each iteration. Initially ~ is any positive definite
matrix approximating the inverse of d~/d~~r. In subsequent iterations
E is improved, using the information built up about the function so that
ultimately E converges to an approximation of the inverse of d2H/d~d~r
at the minimum. If there are many parameters, the number of iterations
may be excessive, but can be considerably decreased by the provision of a
good initial estimate of E. Such an estimate may be obtained by inverting
the information matrix
where e(d
2G/d:red:re r) is obtained from
e(d
2
Fj2friJAr) R: e(dFjd"A dF/d[)t)
~ - ~
(11)
(12)
9. -7-
as described above. When the minimum of H has been found, the inverse
of the information matrix m~y be computed again to obtain standard errors
of all the parameters in ~. A general method for obtaining the elements
of e(dF/dAdF/dAt ) is given in Appendix A2..
- -
The application of the Fletcher-Powell method requires formulas for
the derivatives of F with respect to the elements of ~, r, ~, !'
~o and §E . These may be obtained by matrix differentiation as shown in
Appendix AI. Writing A:;;; B-1, D:;;; B-~ and
(13)
the derivatives are
dF~ :;;; N(Drn D + Drn + n D + n )
- - -yy- - -yx -xy- -xx
(14)
(16)
(17)
(18)
In these expressions we have not taken into account that ~ and :t are
symmetric and that ~~ and e are diagonal matrl·ces. Th f'~ .
·-u -E e 0 ~-dlagonal
10. -8-
zero elements of ~~ and ~E are treated as fixed parameters and the off-
diagonal elements of ¢ and ! as constrained parameters.
When the maximum likelihood estimates of the parameters have been obtained}
the goodness of fit of the model may be tested} in large samples} by the
likelihood ratio technique. Let H
O
be the null hypothesis of the model
under the given specifications of fixed, constrained and free parameters_
The alternative hypothesis HI may be that ~ is any positive definite
matrix.
Under HI' the maximum of log L is (see e.g.) Anderson} 1958, Chapter
log Ll '= -~ N(log 121 + m + n)
Under H
a , the maximum of log L is equal to minus the minimum value
Fa of F. Thus minus 2 times the logarithm of the likelihood ratio
becomes
u '= 2F - N loglSI - N(m + n)
a ~
If the model holds, U is distributed} in large samples} as
1
d = 2 (m + n)(m + n + 1) - r
2 .
X wJ.th
(20 )
(21)
degrees of freedom} where} as before, r is the total number of independent
parameters estimated under H .
o
11. -9-
4. The Special Case of No Errors of Measurement
If there are no errors of measurement in y and x, the model (1)
may be written
By :::: rx + u (22)
where we have written u instead of s. In (22) we have altered the model
slightly, compared to (1), (2) and (3), in that the mean vectors have been
eliminated. This is no limitation, however, since constant terms in the
equations can be handled by using an x -variable that has the value 1 for
every observation. In this case, of course, S should be the raw moment
matrix instead of the dispersion matrix.
This type pf model has been studied for many years by econometricians
under the names of causal chains and interdependent systems (e.g., Wold &
Jureen, 1953). The variables y and x are economic variables and in
the econometric terminology, the variables are classified as exogenous
and endogenous variables, the idea beign that the exogenous variables
are given from the outside and the endogenous variables are accounted for
by the model. From a statistical point of view the distinction is rather
between the independent or predetermined variables ~ and the dependent
variables ~. The residual u represents a random disturbance term assumed
to be uncorrelated with the predetermined variables. Observations and
on y and x are usually in the form of a time series.
Equation (22) is usually referred to as the structural form of the
model. When (2'2) is premultiplied by ~-l one obtains the reduced form
12. Y=~+B-*
-10-
, (23)
where * B-1 ....l(.
u = u. l,I." is the vector of residuals in the
reduced form.
In this case, ~5 and ~€ in (4) are zero and therefore I~l and
E-l in (7) can be written explicitly. It is readily verified that
and
Using these results, log L becomes
If ~ is unconstrained, maximizing log L with respect to ¢ gives
"
CI? == S ,which is to be expected, since CI? in this case is the variance-
~ ~XX
covariance matrix of x After the likelihood has been maximized with
respect to ~, the reduced likelihood is equal to a constant plus
(24)
13. -11-
If also * is unconstrained, further simplification can be obtained,
for then (24) is maximized with respect to *, for given Band!.:,
when 1V is equal to
,
so that the function to be maximized with respect to B and r becomes a
constant plus
log L** == -~ N[ log I~rI - log IBI2
]
1
log( I * I / IB1
2
)
== -"2 N
- -
== _IN log IB-l*Bt -11
2 - --
where
, (26)
1r*== S
-yy
- S II' - lIS + lIS II'
-yx- --xy __xx_
In deriving (26), we started from the likelihood function (7) based on
the assumption of multinormality of y- and !. Such an assumption may be
very unrealistic in most economic applications. Koopmans, Rubin and Leipnik
(1950) derived (24) and (26) from the assumption of multinormal residuals.
~ , which is probably a better assumption. However, the criterion (26)
has intuitive appeal regardless of distributional assumptions and con-
nections with the maximum likelihood method. The matrix l' in (25) is the
variance-covariance matrix of the residuals u in the structural form (22)
14. -12-
and the matrix W* in (27) is the variance-covariance matrix of the residuals
u* in the reduced form (23). Maximizing (26) is equivalent to minimizing
IW*I • Since IW*I is a generalized variance, this method has been called
the full information least generalized residual variance (FILGRV) method
(see, e.g., Goldberger, 1964, Chapter 7). Several other estimation criteria
based on W* have been proposed. Brown (1960) suggested the minimization
of tr(W*) and Zellner (1962) proposed the "minimization of tr(~-l!,*)
where ¥. is proportional to S = S - S S-lS Malinvaud (1966,
.- -yy·x -yy -yx...xx~xy
Chapter 9) considered the family of estimation criteria tr (.s.t*) with
arbitrary positive definite weighting matrices ~.
Since the original article by Koopmans, Rubin and Leipnik (1950) several
authors have contributed to the development of the FILGRV method (Chernoff
& Divinsky, 1953; Klein, 1953, 1969; Brown, 1959; Eisenpress, 1962; Eisenpress
& Greenstadt, 1964 ; Chow, 1968; Wegge, 1969). This paper will add another
computational algorithm to those already existing.
Minimizing 1**1 is equivalent to minimizing
(28)
Matrix derivatives of F with respect to B and r may be obtained by
matrix differentiation as shown in Appendix A3. The results are
(30 )
dFfO~ = 2!-l(~yy - ~xy) _ J;i~ -1 (29)
"" 21V-lem - BS )
~ --xx ~~YX
of/dr
The function F is to be minimized with respect to the elements of
B and E' taking into account that some elements are fixed and others are
constrained in some way. As will be demonstrated in sections 5 and 6,
15. -13-
allowing for equalities among the elements of ~ and r, is not sufficient
to handle some economic applications. Instead, more general constraints may
be involved. Usually these constraints are linear but even models with
nonlinea~ constraints have been studied (see, e.g., Klein, 1969). Such
constraints can be handled as follows.
Let ~I = (~1'~2""J~q) be the vector of all nonfixed elements in
B and r. Each of these elements may be a known linear or nonlinear
function of K' = (Kl,K2
, ••• ,Kr
) , the parameters to be estimated, i.eo,
i 1,2, ••• ,q (31)
Then F is regarded as a function H(K) of The derivatives
of H of first and second order are again given by (9) and (10), but now
K is the matrix of order q x r whose ijth element is df./dK .•
i. J
The
function H(~) may be minimized by the Fletcher-Powell method as before.
The advantage of this method compared to the more general one of the
preceding section is that the function now contains many fewer parameters
and the minimization is therefore faster. The Fletcher-Powell algorithm
is relatively easy to apply even in the nonlinear case and the iterations
converge quadratically from an arbitrary starting point to a minimum of
the function, although there is no guarantee that this is the absolute
minimum if several local minima exist.
5. Analysis of Artificial Data
The following hypothetical economic.model is taken from Brown (1959),
(32a)
16. -14-
(32b)
::: Y (32c)
C + E ::: Y
where the dependent variables are
C ::: consumer expenditures
W ::: wage-salary bill
IT ::: nonwage income
Y = total income, production and expenditure
and the predetermined variables are
T ::: government net revenue
g
E ::: all nonconsumer spending on newly produced final goods
Y-
l
::: value of Y lagged one time period
(32d)
and where and are random disturbance terms assumed to be uncor-
related with the predetermined variables. This hypothetical model will be
used to illustrate some of the ideas and methods of the previous sections.
To begin with we shall assume that the variables involved in this model
are not directly observed. Instead they are assumed to represent true vari-
ables that can only be measured with errors. Such an assumption may not be
unreasonable, as pointed out by Johnston (1963):
To be realistic we must recognize that most economic statistics
contain errors of measurement, so that they are only approximations
to the underlying "true" values. Such errors may arise because
totals are estimated on a sample basis or, even if a complete
enumeration is attempted, errors and inaccuracies may creep in.
Often, too, the published statistics may represent an attempt to
measure concepts which are different from those postUlated in the
theory (p. 148).
17. -15-
Converting the variables to deviations from mean values and writing
~' = (C,W,IT,Y), E' = (Tg,E,y_l) and S' = (ul , U
2,O,O) , model (32) may
be written in the form of (1) as
1 -a -a
°
1 2
° 1
° °
° .1 1 ~
°
~ + S
1
° ° -1
There are 19 independent pat'ameters in this model, namely 4 in Band r
6in
cP = , (34)
3 in
2
a
u
l
2
a a
! '"
u
lu2
u
2 (35)
,
° ° 0
0 0
° °
and 6 in~6 = diag(8
T
,8
E,8y ) and ~€ = diag(8c,eW
,eIT,oy) Note that
s -1
since (32c) and (32d) are error-free equations, * has the form (35) with
zero variances and covariances for ~. and u4 Also since Y-l is Y
lagged, we have assumed that the error variances in Y and Y-l are the
same. Therefore, @6 and ~€ have only 6 independent elements.
18. -16-
Data were generated from this model by assigning the following values
to each of the 19 parameters
al = 0.8 a
2
= 0.4 b
1
= 0.:; be = 0.2
2
= 1.0
2
~ = :;.0
CT
T
CT
E
= 2.0
g -1
CT
T E = 0.1 crT Y = 0.2 CT
EY
= 0.1 •
.g g -1 -1 (:;6)
2
= 0.2
2
= 0.:; = 0.1
cr CT cr
u
1
u
2
u
1u2
eT
= 0.4 e =0.6 ey = 0·5
g E -1
ec = 0·5 ew= 0.6 err = 0·9 e = 0·5
y
The resulting ~ , obtained from (4) and rounded to :; decimals, is
C W rr y T E Y
c 4·599 g -1
W 2.481 2.069
rr 4.659 2.159 7·514
y 6.449 :;·7:;1 7.409 10·799 (37)
T -0.692 -0.138 -1.454 -0·592 1.160
g
E 2.100 1.250 2·750 4.100 0.100 2.:;60
Y-l
0.442 0·763 -0.421 0·542 0.200 0.100 3.250
For the purpose of illustrating the estimation method of section 3, the
above matrix is regarded as a sample dispersion matrix S to be analyzed.
The order of the vector A is 78, since there are 78 elements in B, [',
p, *", §;)e and §€ all together. Of these, 54 are fixed and. 24 are
nonfixed, so that ~ is of order 24. Because of the symmetry of ~ and ~
and the imposed equality of ey and ey ,there are 19 independent param-
-1
eters, so that the order of ~ is 19.
19. -17-
The minimization o~ H(~) started at the point
al '" 0.6 , a2 '" 0·3 , b '" 0.4 , b2 '" 0.1
1
2
2.0
2 2.0 2 2.0
(TT '" , (TE :=; , (Ty '"
g -1
(TT E :=
(TT Y :=
(TEY = 0.0
g g -1 -1
2
0·3 » '" 0·3 0.0
(T = , , (T '"
u
l
u
2 ~u2
ST :=; 0.4 , e
E
:= 0.6 , ey := 0·5
g -1
e '" 0·5 , ew= 0.6 , @rr = 0·9 , ey = 0.5
c
From this point seven steepest descent iterations were per~ormed. There-
a~ter Fletcher-Powell iterations were used and it took 23 such iterations
to reach a point where all derivatives were less than 0.00005 in absolute
value. At this point, the solution was correct to four decimals and
the ~ in (37) was reproduced exactly. Twenty-three Fletcher-Powell
iterations required ~or convergence is not considered excessive since no
in~ormation about second-order derivatives was used and it takes at least
19 Fletcher-Powell iterations to build up an estimate o~ the matrix o~
second order derivatives.
We now consider model (32a-d) in the case when the variables are
observed without errors o~ measurement. Then the method o~ section 3
cannot be applied directly since the two identities (32c) and (32d) imply
that 2: is singular. There~ore, two o~ the endogenous variables must be
eliminated ~rom the system. It seems most convenient to eliminate C and
Y. When these variables have been eliminated, the structural equations
become
20. -18-
- b
1
(58)
This system may be estimated by the method of section 4.
To illustrate the application o~ the estimation procedure we use a
dispersion matrix S obtained ~rom ~ in (37) by subtracting the error
variances from the diagonal elements and deleting rows and columns corres-
ponding to C and Y. There are 6 nonf'Lxed elements in Band !',
These elements are functions of'
namely I3U
'
o~ the vector
1312 '
:rc •
1321 ) and These are the elements
and
b2 defined by [compare equation (31)]
1311
-1 0 0 0
1312
0 -1 0 0 a
l
1321
0 0 -1 0 a
2
1
::: + (39)
1322
0 0 -1 0 b
l
0
0 0 1 0 b
2
0 0 0 1
Thus the function F is a function o~ 4 independent parameters.
The function F was'minimized using only Fletcher-Powell iterations
starting from the point
The solution point, found after 8 iterations, was, as expected, a
l
= 0.8 ,
a
2
::: 0.4) b
l
= 0·3, b
2:::
0.2 with
21. A (0.2
1jr*=
0.1
0.1)
0·3
-19-
I~I = 0.05
6. An Economic Application
In this section we apply methods SFLGRV and RFLGRV to a small economic
model taken from the literature. The model is Klein's model of United
States economy presented in Klein (1950, Pl'. 58-66):
C == aO + alP + a2P_l + a,w + ~
I == bO + blP + b2P_l + b
3K_l
+ u
2
W* == Co + clE + c2E_l + c
3A
+ u:;
Y + T == C + I + G
Consumption:
Investment:
Private wages:
Product:
Income:
Capital:
Wages:
Private product:
y=p+w
K == K_l + I
w==W*+W**
E=Y+T-W** ,
(40a)
(40b)
(40c)
(40d)
(40e)
(40f)
(J+Og )
(40h)
where the endogenous variables are
C == consumption
I == investment
~. == private wage bill
P == profits
Y == national income
K = end-of-year capital stock
W == total wage bill
E = private product
22. -20-
and the predetermined variables are the lagged endogenous variables P-l,
K_
l
and E_
l
and the exogenous variables
1 ::: unity
W** == government wage bill
T indirect taxes
G == government expenditures
A = time in years :from 1931.
All variables except 1 and A are in billions of 1934 dollars.
This model contains eight dependent variables and eight predetermined
variables. There are three equations involving residual terms. The other
five equations are identities. Using the five identities (40d) - (40h),
P, Y, K, W and E may be solved for and substituted into (40a) -
(40c). This gives a model with the following structural form
C
- a - a a
l
- ~)
(:)
1 1
- b 1 - b b
l
1 1
- c - c 1
1 1
1
T
(0 a
3
- a -a a
1
0 a
2
0
~j
G
1 1
:::: b
O
- b -b b
l
0 b
2
b
3
A (41)
1 1
Co - c 0 c
1 c
3
0 0 P-l
1
K_l
E_l
There are 24 nonfixed elements in B and r . These are all linear
~
functions of the 12 unknown coefficients in (40a-c) as follows
24. -22-
C I W*
1 1133·90 26.60 763.fJJ
W** 5977·33 103.80 4044.07
T 7858.86 160 .40 5315·62
G 11633.68 243.19 7922.46
s =
A 577·70 -105.60 460·90
-xy
P-1 18929·37 655·33 12871·73
K_1 227767·38 5073·25 153470·56
E_1 66815·25 1831.13 45288·51
1 W** T G A P K_
1
E_
1
1 21.00 -1
W** 107·50 626.87
T 142·90 789·27 1054·95
G 208.20 1200.19 1546.11 2369·94
s = 238.00 176.00 421·70
-ocx A 0.00 770.00
P-1 343·90 1746~22 2348.48 3451.86 -11·90 5956.29
K_
1 4210.40 21683.18 28766.23 42026.14 590.60 69073·54 846132·70
E_1 1217·70 6364.43 8436·53 12473·50 495·60 20542.22 244984·77 72200.03
The ~o11owing estimated model was obtained
with
C = 18.318 - 0.229P + 0.384p + 0.802W + u "1",
-1 1
I = 27·278 - 0·797P + 1.051P_1 - 0.l48K_1 + ~ I
W* = 5·766 + 0.235E + 0.284E_1 + 0.234A + ~ .~
(43)
(44)
25. -23-
The standard errors of the estimated parameters may be obtained from a
formula for the asymptotic variance-covariance matrix developed by Rothenberg
and Leenders (1964).
7. The Special Case of No Residuals
When there are no residuals in (1), the relations between ~ and ~
are exact. The joint distribution of ~ and ~ is singular and of rank n •
vanishes. In general)
and r or in ~ e
-5
and e ) this model has to be estimated by the method of section 3. This
~E
In the equation (4) for ~ ) the second term in ~
-yy
when there are fixed and constrained elements in B
may be done by choosing 0/ = 0 and specifying the fixed elements and the
constraints as described in that section.
The matrix ~ can also be written
,
where
e = (~E Q) ,
Q ~5
(46)
from which it is seen that the model is identical to a certain restricted
factor analysis model. Several special cases will ~ow be considered.
If B = I and r is unconstrained, i.e., all elements of r are
- -
regarded as free parameters, model (45) is formally equivalent to an un-
restricted factor model (JClreskog, 1969). The matrix i. in (46) may be
obtained from any 0* of order (m + n) x n satisfying
26. -24-
2
r. == 11.*/1,*' + e
by a transformation of 11.* to a reference variables solution where the x's
are used as reference variables. Maximum likelihood estimates of 11.* and e
may be obtained by the method of JBreskog (1967a,b) which also yields a large
sample x
2
test of goodness of fit. Let the estimate of 11.* be partitioned
as
, (48)
where is of order m x n and ~ of order n x n • Then the maximum
likelihood estimates of r and ~ are
(50 )
If B == I and r is constrained to have some fixed elements while the
remaining elements in r are free parameters, model (45) is formally equiva-
lent to a restricted factor model in the sense of JBreskog (1969). This model
may be estimated by the procedure described in the same paper and, in large
samples, standard errors of the estimates and a goodness of fit test can also
be obtained. A computer program for this procedure is available (JBreskog &
Gruvaeus, 1967).
A more general case is when B is lower triangular. The structural
equation system for the true variates is then a causal chain. In general
such a causal chain may be estimated by the method described in section 3
27. -25-
of the paper, though there may be simpler methods. One example occurs when
the system is normalized by fixing one element in each row of r to unity
and B has the form
B :
o
1322
o
••• 0
where all the I3ls are free parameters. Then there is a one-to-one trans-
formation between the free parameters of B and the free elements of
A : B-1• One may therefore estimate A instead of B. In this case,
the variance-covariance matrix ~ is of the form
where
Model (51) is a special case of a general model for covariance structures
developed by J8reskog (1970) and may be estimated using the computer program
ACOVS (J8reskog, Gruvaeus & van Thillo, 1970). In this model £, 1?
and e may contain fixed parameters and even parameters constrained to be
-€
equal in groups. The computer program gives maximum likelihood estimates of
the free parameters in ~, r , and, in large samples,
standard errors of these estimates and a test of overall goodness of fit of
the model can also be obtained.
28. -26-
More generally, the above mentioned method may be used whenever ~
can be written in the form (51) such that there is a one-to-one correspondence
between the free parameters in Band r and the distinct free elements in
B* and A. For a less trivial example, see J8reskog (1970, .section 2.6).
8. A Psychological Application
In this section we consider a simplified model for the prediction of
achievements in mathematics (M) and science (S) at different grade levels.
To estimate the model we make use of longitudinal data from a growth study
conducted at Educational Testing Service (Anderson &Maier, 1963; Hilton,
1969). In this study a nationwide sample of fifth graders was tested in
1961 and then again in 1963, 1965 and 1967 as seventh, ninth and eleventh
graders, respectively. The test scores employed in this model are the
verbal (V) and quantitative (Q) parts of SCAT (Scholastic Aptitude
Test) obtained in 1961 and the achievement tests in mathematics (M5,~,M9'
M
l l
) and science (S5,S7,8
9,Sll)
obtained in 1961, 1963, 1965, and 1967,
respectively. The achievement tests have been scaled so that the unit of
measurement is approximately the same at all grade levels.
The model is depicted in Figure 1, where V
M
l l
, 8
5
,
~l' S2' •• " ~8
is
8
7
, 8
9
and 8
11
denote the true scores of the tests and
the corresponding residuals. The model for the true scores
(53a)
(53b)
29. -27-
This model postulates the major influences of a student's achievement in
mathematics and science at various grade levels. At grade 5 the main
determinants of a student 's achievements are his verbal and quantitative
(53c)
(53d)
(53e)
(53g)
(53h)
abilities at that stage. At higher grade levels, however, the achievements
are mainly determined by his achievements in the earlier grades. Thus,
achievements in mathematics in grade i is determined mainly by the
achievements in mathematics in grade i - 2 , whereas achievements in sci-
ence in grade i is determined mainly by the achievements in science in
grade i - 2 and in mathematics in grade i, i = 7,9,11 •
The structural form of this model is
1 0 0 0 0 0 0 0 M
5
a
1
a
2 Sl
0 "I 0 0 0 0 0 0 8
5
b
l
b
2 s2
-c 0 1 0 0 0 0 0
~
0 0 S3
1
0 -d -d 1 0 0 0 0 8
7
0 0
(:) +
S4
1 2 =
0 0 -e 0 1 0 0 0 M
9
0 0
S5
1
0 0 0 -f -f 1 0 0 8
9
0 0 S6
1 2
0 0 0 0 -gl 0 1 0 MIl 0 o I S7
0 0 0 0 0 -hI -h 8
11
0 o I Sa
2 J
30. -28-
It is seen that this model is a causal chain. The model can be estimated by
the method described in section 5, provided some assumption is made about the
intercorrelations of residuals Sl'S2' ••• 'S8. Without such an assumption
the model is not identified. We have chosen to make the assumption that
all residuals are uncorrelated except Sl and S2· This assumption does
not seem to be too unrealistic.
The data that we use consist of a random sample of 730 boys taken from
all the boys that took all tests at all occasions. The variance-covariance
matrices are
M
5
130.690
115·645
116.162
90·709
119·564
104.430
119·712
90·916
179·617
125.858·
114.364
l25·223
135·074
126.470
116·950
193·557
120.426
155·885
157.827
149·930
117.439
148.648
120.492
133·231
112.218
109·187
215·894
159·783
175·497
155.839
218.067
149·045
147.115
264.071
143·218 190.763
,
~
106.837
87·859
8
9
107.75°
72·534
MIl
107·042
89·617
,
v
s = V (.158.014
~xx Q 73.518
The estimated model is
~ "= o.64ov + 0.415Q + ~1 (55a)
(55b)
(55c)
31. -29-
8
7
= 0.3258
5 + 0.493M7 + ~4
M
9
= 1.027M7 + ~5
(55d)
(55e)
(55f)
(55g)
(55h)
The estimated variance-covariance matrix of the true scores V and Q is
V
A V (105048
(l> = Q 73.95
Estimated residual variances and error variances for each measure are given
below
Measure
V
Q
M
5
8
5
M
7
8
7
M
9
8
9
M
l l
811
Residual Variance
10.0
22·5
26.4
29·5
25·2
28·5
75·7
20.0
Error Variance
}?l
4.4
25.4
11.8
40·3 .
24.3
29·3
36.1
18.8
47.7
32. -30-
The estimated correlation between ~l and ~2 is 0.17·
The estimated reduced form for the true scores is
M = 0.640v + 0.415Q + ~*
5 1
~ = 0·70211 + 0.455Q + ~3
M
9
= 0·721V + 0.467Q + ~5
8
9
= 0.815V + 0.296Q + ~6
8
11 = 0.663V + 0.277Q + ~8
The relative variance contributions of V and Q, the residual ~* and
the error, to each test's total variance are shown below:
Measure V and Q Residual Error
M
5
0·73 0.08 0.19
8
5
0.78 0.15 0.07
~ 0·59 0.20 0.21
8
7 0·56 0.28 0.16
M
9
0·56 0.30 0.14
8
9
0·52 0.32 0.16
M
l l
0.42 0·51 0.07
8
11 0.42 0·33 0.25
(56a)
(56c)
(56e)
(56f)
(56g)
(56h)
33. -31-
It is not easy to give a clear-cut interpretation of these results.
Inspecting first the equations (55c), (55e) and (55g), it is seen that.a
unit increase in M. 2 tends to have a smaller effect on M. the larger
1.- 1.
i is. This agrees with the· fact that the growth curves in mathematics
"flattens" out at the higher grade levels. One would expect that the co-
efficient in (55g), like in (55c) and el in (55e), would be
greater than one, since, in general, for these data, the correlation of status,
M. - M. 2 ' are positive although usually very small.
1. 1.-
However, the large residual variance ~7 suggests that M
9
alone is not
sufficient to account for M
l l•
This is probably due to the fact that
mathematics courses at the higher grades change character from being
mainly Itarithemetic computation" to involving more "algebraic reasoning."
Inspecting next the equations (55d), (55f) and (55h) describing
science achievements, it is Seen that the influence of mathematics on
science tends to decrease at the higher grades. This is natural since
science courses in the lower grades are based mainly on "logical reasoning"
whereas in the higher grades they are based on "m
emorizing of facts. It The
effect of science achievements on science two years later first increases
and then decreases. This is probably because the science courses special-
ize into different courses (Biology, Physics, etc.) at grade 11 whereas
the science test at the lower grades measures some kind of overall 11science
knowledge."
Whatever may be the best interpretations of these results, the example
serves to illustrate that it is possible to have both errors in equations
and errors in variables and still have an estimable model.
34. -32-
References
Anderson, T. W. An introduction to multivariate statistical analysis. New
York: Wiley, 1958.
Anderson, S. B., &Maier, M. H. 34,000 pupils and how they grew. Journal
of Teacher Education, 1963, ~ 212-216.
Blalock, H. M. Causal inferences in nonexperimental research. Chapel Hill,
N. C.: University of North Carolina Press, 1964.
Brown, T. M. Simplified full maximum likelihood and comparative structural
estimates. Econometrica, 1959, g]j 638-653.
Brown, T. M. Simultaneous least squares: a distribution free method of
equation system structure estimation. International Economic Review,
1960, b 173-191.
Chernoff, H., & Divinsky, N. The computation of maximum-likelihood estimates
of linear structural equations. In W. C. Hood & T. C. Koopmans (Eds.),
Studies in econometric method, Cowles Commission Monograph 14. New York:
Wiley, 1953. pp. 236-269.
Chow, G. C. Two methods of computing full-information maximum likelihOod
estimates in simultaneous stochastic equations. International Economic
Review, 1968, 2." 100-112.
Eisenpress, H. Note on the computation of full-information maximum-likelihood
estimates of coefficients of a simultaneous system. Econometrica, 1962,
2£, 343-348.
Eisenpress, H., & Greenstadt, J. The estimation of non-linear econometric
systems. Econometrica, 1966, ~ 851-861.
35. -33-
Fletcher, R., & Powell, M. J. D. A rapidly convergent descent method ~or
minimization. The Computer Journal, 1963, £, 163-168.
Goldberger, A. S. Econometric theory. New York: Wiley, 1964.
Gruvaeus, G., &J8reskog, K. G. A comPuter program for minimizing a function
o~ several variables. Research Bulletin 70-14. Princeton, N. J.:
Educational Testing Service, 1970.
Hilton, T. L. Growth study annotated bibliography. Progress Report 69-11.
Princeton, N. J.: Educational Testing Service, 1969.
Johnston, J. Econometric methods. New York: McGraw-Hill, 1963.
J8reskog, K. G. Some contributions to maximum likelihood factor analysis.
Psychometrika, 1967, ~ 443-482. (a)
J8reskog, K. G. UMLFA--A computer program for unrestricted maximum likelihood
~actor analysis. Research Memorandum 66-20. Princeton, N. J.: Educational
Testing Service, revised edition, 1967. (b)
J8reskog, K. G. A general approach to confirmatory maximum likelihood factor
analysis. Psychometrika, 1969, ~ 183-202.
J8reskog, K. G. A general method for analysis of covariance structures.
Biometrika.. 1970, 2L 239-251-
J8reskog, K. G., & Gruvaeus, G. RMLFA--A computer program for restricted
maximum likelihood factor analysis. Research Memorandum 67-21. Princeton:
N. J.: Educational Testing Service, 1967.
J8reskog, K. G., Gruvaeus, G. T., & van Thil1o, M. ACfJVS--A general computer
program ~or analysis of covariance structures. Research Bulletin 70-15.
Princeton, N. J.: Educational Testing Service, 1970.
36. -34-
JBreskog, K. G., & van Thill0, M. LISREL--A general computer program for
estimating linear structural relationships. Research Bulletin 70-00.
Princeton, N. J.: Educational Testing Service, in preparation.
Klein, L. R. Economic fluctuations in the United States, 1921-1941, Cowles
Commission Monograph 11. New York: Wiley, 1950.
Klein, L. R. A textbook of econometrics. Evanston: Row, Peterson, 1953.
Klein, L. R. Estimation of interdependent systems in macroeconometrics.
Econometrica, 1969, 2L 171-192.
Koopmans , T. C., Rubin, H., & Leipnik, R. B. Measuring the equation systems
of dynamic economics. In T. C. Koopmans (Ed.), Statistical inference
in dynamic economic models, Cowles Commission Monograph 10. New York:
Wiley, 1950. pp. 53-237·
Malinvaud, E. Statistical methods of econometrics. Chicago: Rand-McNally,
1966.
Rothenberg, T. G., & Leenders, C. T. Efficient estimation of simultaneous
equation systems. Econometrica, 1964, 32, 57-76.
Turner, M. E., & Stevens, C. D. The regression analysis of causal paths.
Biometrics, 1959, Q, 236-258·
Wegge, L. L. A family of functional iterations and the solution of maximum
likelihood estimation equations. Econometrica, 1969, 2L 122-130.
Werts, C. E., & Linn, R. L. Path analysis: Psychological examples.
Psychological Bulletin, 1970, Ii (3), 193-212.
Wold, H., & Jureen, L. Demand analysis. New York: Wiley, 1953.
Zellner, A. An efficient method of estimating seemingly unrelated
regressions and tests for aggregation bias. Journal of the
American Statistical Association, 1962, 21, 348-368.
37. -35-
A. Appendices of Mathematical Derivations
Al. Matrix Derivatives of Function F in Section 3
The function is
(Al)
which is regarded as a function of B, r, ~, ~, ~o' ~E defined by
(4). To derive the matrix derivatives we shall make u£e of matrix dif-
ferentials. In general, dX = (dx
i j
) will denote a matrix of differentials
and if F is a function of X and dF = tr(Q~t) then dF/d~ = Q •
-1 1
Writing A = Band Q== ~- r = AI' we have
= Adf' - AdBAr
== Adf' - AdBD
Furthermore, since in general,
(A2)
(A3)
and
dlog Ixl
38. -36-
we obtain from (Al),
(A4)
where n is defined by (12) and dE is partitioned the same way as n in
(12) .
From (~-) and the def'initicns of' A and D we have
z :::: DPDf + ~'k~t
2
+ e
-yy -€
L. :::: r;' :::: ~:QI
-xy -yx
L. ::::
~ + r£<}
-xx -5
(A5)
(A6)
from which we obtain
dL. :::: D~dD' + Dd~D' + dD~D'
-yy
+ A'VdAt + Ad1jrA' + dA1jrAI
+ 213 de
_E ~e
(AB)
(A10)
39. -37-
Substitution of dA and dD from (A2) and (A3) into (AB) and (A9)
gives
+ AdI1!D' - AdB])1lD'
- A"ljrA'dB'A' - AdBA1jfAt
+ Dd<PD' + Ad1jfA' + 2e de
. -€ -€
, (All)
c1>D rdB'A' + d<!JDt (A12)
Substitution of (All), (A12) and (AIO) into (A4), noting that tr(CtdX)
= tr(~rg) = tr(2~t) and collecting terms, shows that the matrices multiplying
d!lt, dr', d~, dljr, d~8 and d@€ are the matrices on the right sides of
equations (14), (15), (17), (18) and (19) respectively. These are therefore
the corresponding matrix derivatives.
A2. Information Matrix for the General Model of Section 3
In this section we shall prove a general theorem concerning the expected
second-order derivatives of any function of the type (8) and show how this
theorem can be applied to compute all the elements of the information matrix
(12).
We first prove the following
N
Lemma: Let S = (liN) ~l (~a - ~)( ~a - ~) r ). where ~l' ~2' ••• , ~N are
independently distributed according to N(~,~). Then the asymptotic
40. -38-
distribution of the elements of n = E-l(E - S)~-l is multivariate
..... ..... -- ......-
normal with means zero and variances and covariances given by
(A13)
Proof: The proof follows immediately by multiplying w~ = E E ~ag(~gh - Sgh)~h~
. . g h
and w = E E ~l(~.. - s .. )crJ V and using the fact that the asymptotic vari-
I-J.V .. lJ lJ .
1 J
ances and covariances of S are given by
Ne [(cr h - s h)(cr.. - s . .)]
g g lJ lJ
(see e.g., Anderson, Theorem 4.2.4).
IT .~ • + C1 ~
gl hJ gj hi
We can now prove the following general theorem.
Theorem: Under the conditions of the above lemma let the elements of E be
functions of two parameter matrices ~ = (I-J.
gh
) and N = (Vi j
)
let F(~,~) = ~ N[logl~1 + tr(§~-l)] with OF/O~ = NAnB and
OF/O~ = N~p. Then we have asymptotically
and
( / ) ( ,2 ,I " ) -1) ( -1) ( ~'"-In) . (B! E-Ie! )h' . ( 4)
1 Ned 11) OI-J. hOv.. =(IV:, e I • B!E D h' + ~ Al
g lJ gl J gJ ].
Proof: Writing OF/OI-J.gh = Na~p)O:p'bAh and OF/Ov .. = Nc, w d . , where it
g..;:. I f-' f-' lJ ljl I-J.V YJ
is assumed that every repeated subscript is to be summed over, we have
2
(I/N)e(o F/Ojl hOY .. ) = (l/N)e(OF/OI-J. hOF/Ov.. )
g lJ g lJ
= N e(a~P)NAbA~c. w d j)
g..;:. ~ pr; au I-J.v V
= N a~bAhc. d .e(w~w )
o~ f-' ll-J. vJ "'1-" I-J.v
41. -39-
It should be noted that the theorem is quite general in that both M
and N may be row or column vectors or scalars and M and N may be
identical in which case, of course, ~ ~ 9 and B =D •
We now show how the above theorem can be applied repeatedly to compute
all the elements of the information matrix (12). To do so we write the
derivatives (14) - (19) in the form required by the theorem.
Let A ~ B-1 and ~ ~ ~-l~ , as before, and
T[m x (m + n)] ~ [A' 0]
P[(m + n) x m] ~~~' + ~!~J
<PD'
Q[(m + n) x n]
.(;)
~[(m + n) x n] • G)
Then it is readily verified that
dF/OB ~ -NT.I1P
dF/dr ~ NT.I1Q
dF/d(J) = NR'.I1R
(A15)
(Al6)
(A18)
(A20)
(A21)
(A22)
42. -40-
dF/d8 :::: Nne (A23)
In the last equation we have combined (18) and (19) using 8 ::::~o 9.
- ~ 8J
A3. Matrix Derivatives of Funct:i,on F in Section 4
The function is defined by
,
where
One finds immediately that
( -1 ) -1 )
dF :::: tr ~ d~ - 2tr(B dB
"'" "'" -"....
:::: tr[~-l(dBS Bt + BS dB' - dES r t - rs dB')]
- ~~YY- --yy - ~-yx- --x:y-.' ~
-1
- 2tr(!? d~)
,
so that the derivatives dFf2JB and dFf2Jr are those given by (29) and
- ~
()O ).