This document presents a method for calculating asymptotic confidence intervals for indirect effects in structural equation models. The author first shows that under maximum likelihood estimation, the coefficient vector in a recursive structural equation model is asymptotically normally distributed. Then, using the multivariate delta method, it is shown that nonlinear functions of the coefficients, such as indirect effects, are also asymptotically normally distributed. This allows confidence intervals to be constructed for indirect effects based on their asymptotic distribution. An example is worked through to demonstrate the method.
APPLICATION OF VARIABLE FUZZY SETS IN THE ANALYSIS OF SYNTHETIC DISASTER DEGR...ijfls
This paper proposes a fuzzy multi -criteria decision making method for disaster risk analysis. It considers the application of “-cut” and “fuzzy arithmetic operations” to rank the fuzzy numbers describing linguistic variables. This article studies the use of variable fuzzy sets and is applied to study disaster area of Nagapattinam district under north- east monsoon rainfall. The relative and synthetic disaster degree is obtained for the places under study.
Application of Weighted Least Squares Regression in Forecastingpaperpublications3
Abstract: This work models the loss of properties from fire outbreak in Ogun State using Simple Weighted Least Square Regression. The study covers (secondary) data on fire outbreak and monetary value of properties loss across the twenty (20) Local Government Areas of Ogun state for the year 2010. Data collected were analyzed electronically using SPSS 21.0. Results from the analysis reveal that there is a very strong positive relationship between the number of fire outbreak and the loss of properties; this relationship is significant. Fire outbreak exerts significant influence on loss of properties and it accounts for approximately 91.2% of the loss of properties in the state.
DESIGN METHODOLOGY FOR RELATIONAL DATABASES: ISSUES RELATED TO TERNARY RELATI...ijdms
Entity-Relationship (ER) modeling plays a major role in relational database design. The data requirements
are conceptualized using an ER model and then transformed to relations. If the requirements are well
understood by the designer and then if the ER model is modeled and transformed to relations, the resultant
relations will be normalized. However the choice of modeling relationships between entities with
appropriate degree and cardinality ratio has a very severe impact on database design. In this paper, we
focus on the issues related to modeling binary relationships, ternary relationships, decomposing ternary
relationships to binary equivalents and transforming the same to relations. The impact of applying higher
normal forms to relations with composite keys is analyzed. We have also proposed a methodology which
database designers must follow during each phase of database design.
There are subsets of genes that have similar behavior under subsets of conditions, so we say that they
coexpress, but behaveindependently under other subsets of conditions. Discovering such coexpressions can
be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of
utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes
that are coexpressed under clusters of conditions. This type of clustering is called biclustering.
Biclustering is an NP-hard problem. Consequently, heuristic algorithms are typically used to approximate
this problem by finding suboptimal solutions. In this paper, we make a new survey on tools , biclusters
validation and evaluation functions.
APPLICATION OF VARIABLE FUZZY SETS IN THE ANALYSIS OF SYNTHETIC DISASTER DEGR...ijfls
This paper proposes a fuzzy multi -criteria decision making method for disaster risk analysis. It considers the application of “-cut” and “fuzzy arithmetic operations” to rank the fuzzy numbers describing linguistic variables. This article studies the use of variable fuzzy sets and is applied to study disaster area of Nagapattinam district under north- east monsoon rainfall. The relative and synthetic disaster degree is obtained for the places under study.
Application of Weighted Least Squares Regression in Forecastingpaperpublications3
Abstract: This work models the loss of properties from fire outbreak in Ogun State using Simple Weighted Least Square Regression. The study covers (secondary) data on fire outbreak and monetary value of properties loss across the twenty (20) Local Government Areas of Ogun state for the year 2010. Data collected were analyzed electronically using SPSS 21.0. Results from the analysis reveal that there is a very strong positive relationship between the number of fire outbreak and the loss of properties; this relationship is significant. Fire outbreak exerts significant influence on loss of properties and it accounts for approximately 91.2% of the loss of properties in the state.
DESIGN METHODOLOGY FOR RELATIONAL DATABASES: ISSUES RELATED TO TERNARY RELATI...ijdms
Entity-Relationship (ER) modeling plays a major role in relational database design. The data requirements
are conceptualized using an ER model and then transformed to relations. If the requirements are well
understood by the designer and then if the ER model is modeled and transformed to relations, the resultant
relations will be normalized. However the choice of modeling relationships between entities with
appropriate degree and cardinality ratio has a very severe impact on database design. In this paper, we
focus on the issues related to modeling binary relationships, ternary relationships, decomposing ternary
relationships to binary equivalents and transforming the same to relations. The impact of applying higher
normal forms to relations with composite keys is analyzed. We have also proposed a methodology which
database designers must follow during each phase of database design.
There are subsets of genes that have similar behavior under subsets of conditions, so we say that they
coexpress, but behaveindependently under other subsets of conditions. Discovering such coexpressions can
be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of
utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes
that are coexpressed under clusters of conditions. This type of clustering is called biclustering.
Biclustering is an NP-hard problem. Consequently, heuristic algorithms are typically used to approximate
this problem by finding suboptimal solutions. In this paper, we make a new survey on tools , biclusters
validation and evaluation functions.
In this work, using professional software Fuzzy Logic Toolbox from MATLAB 7.5 specifically that shows the simulation of a model that reflects organizational change process based on the approach to complexity at the Center for Environmental Engineering Camagüey (CIAC). This organization, as a social formation itself is immersed in an environment that maintains the mutual relations of influence, and it provides the organizational complexity and multiple aspects of uncertainty or fuzziness of its boundaries. It is through the interaction and interconnection of multiple and different factors in nature that based on the implementation of structural and functional systemic approach using a hermeneutic dialectic epistemology we intend to achieve in practice self-organization of the center. Each of these factors or variables, such as nonlinear phenomena in itself, defined by human thought that is imprecise by nature, is expressed by fuzzy sets with overlapping boundaries, which, together with the rule base (existing knowledge system) and the inference mechanism conforms the fuzzy inference system (FIS) that shapes the future conduct of the Center and the interrelationships between all variables. Integration into a single model of factors as diverse and yet highly interrelated with as participation, co-leadership and autonomy variable as "research & development willingness" and others like impacts, production, relevance and optimization to identify possible capacity variable analysis from a vision trans-disciplinary process of self-organization and school management.
Author Mr Di Chen : Ecole Polytechnique Féderale de Lausanne: Financial Engineering Section
This paper shows that complexity influences stock returns. By establishing the complexity and resilience measure of the common stock and analyzing the relationship between return, momentum, size, complexity, book-to-market ratio and resilience, three measures (size, complexity and momentum) stand out as the factors that can influence stock returns.
Cointegration of Interest Rate- The Case of Albaniarahulmonikasharma
In economic theories, the study of non-stationary time series, takes a special place. These series may have causal links between them in the short and medium term. In this paper, attention focuses on discovering links in the long term. If there are long-term bonds between the series then the series are said that cointegrate. Various tests, such as Engel-Granger's two-steps procedure, three steps Engle-Yoo method, Saikkonen method and Johansen method, will be analyzed. For each of these methods, advantages and disadvantages are given. In the last part of the paper, these methods are applied for real series such as the interest rate on credit and deposit interest ratesfor Albania.
Dimensionality Reduction Techniques In Response Surface Designsinventionjournals
Dimensionality reduction has enormous applications in various fields in industries. It can be applied in an optimal way with respect to time and cost related to the agricultural sciences, mechanical engineering, chemical technology, pharmaceutical sciences, clinical trials, biological studies, image processing, pattern recognitions etc. Several researchers made attempts on the reduction of the size of the model for different specific problems using some mathematical and statistical techniques identifying and eliminating some insignificant variables.This paper presents a review of the available literature on dimensionality reduction
A Critique of Factor Analysis of Interest RatesIlias Lekkos
Any old paper of mine on the limitations of applying factor analysis and principal component analysis on interest rates that has received renewed attention
ABSTRACT : This paper critically examined a broad view of Structural Equation Model (SEM) with a view
of pointing out direction on how researchers can employ this model to future researches, with specific focus on
several traditional multivariate procedures like factor analysis, discriminant analysis, path analysis. This study
employed a descriptive survey and historical research design. Data was computed viaDescriptive Statistics,
Correlation Coefficient, Reliability. The study concluded that Novice researchers must take care of assumptions
and concepts of Structure Equation Modeling, while building a model to check the proposed hypothesis. SEM is
more or less an evolving technique in the research, which is expanding to new fields. Moreover, it is providing
new insights to researchers for conducting longitudinal investigations.
.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Development of a Spatial Path-Analysis Method for Spatial Data AnalysisIJECEIAES
Path analysis is a method for identifying and analyzing direct and indirect relationship be- tween independent and dependent variables. This method was developed by Sewal Wright and initially only used correlation analysis results in identifying the variables’ relationship. So far, path analysis has been mostly used to deal with variables of non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to enhance path analysis to identify the effects of spatial dependencies. This paper proposes a method derived from path analysis that can process data with spatial elements and furthermore can be used to identify and analyze the spatial effects on the data; we call this method spatial path analysis.
PREDICTING CLASS-IMBALANCED BUSINESS RISK USING RESAMPLING, REGULARIZATION, A...IJMIT JOURNAL
We aim at developing and improving the imbalanced business risk modeling via jointly using proper
evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques.
Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison
based on 10-fold cross validation. Two undersampling strategies including random undersampling (RUS)
and cluster centroid undersampling (CCUS), as well as two oversampling methods including random
oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE), are applied. Three highly
interpretable classifiers, including logistic regression without regularization (LR), L1-regularized LR
(L1LR), and decision tree (DT) are implemented. Two ensembling techniques, including Bagging and
Boosting, are applied on the DT classifier for further model improvement. The results show that, Boosting
on DT by using the oversampled data containing 50% positives via SMOTE is the optimal model and it can
achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and 0.8907, respectively.
Information Economics Cost-Benefit Analysis on Automatic Billing System Imple...Trisnadi Wijaya
Information Economics Cost-Benefit Analysis
on Automatic Billing System Implementation
at Ogan Central Electronic
Authors:
Trisnadi Wijaya
Rika Kharlina Ekawati
Published at:
2014 International Conference on Economic and Information System Management Proceedings
ISBN : 978-602-71513-0-7
Proceedings URL : http://eprints.mdp.ac.id/1180/
In this work, using professional software Fuzzy Logic Toolbox from MATLAB 7.5 specifically that shows the simulation of a model that reflects organizational change process based on the approach to complexity at the Center for Environmental Engineering Camagüey (CIAC). This organization, as a social formation itself is immersed in an environment that maintains the mutual relations of influence, and it provides the organizational complexity and multiple aspects of uncertainty or fuzziness of its boundaries. It is through the interaction and interconnection of multiple and different factors in nature that based on the implementation of structural and functional systemic approach using a hermeneutic dialectic epistemology we intend to achieve in practice self-organization of the center. Each of these factors or variables, such as nonlinear phenomena in itself, defined by human thought that is imprecise by nature, is expressed by fuzzy sets with overlapping boundaries, which, together with the rule base (existing knowledge system) and the inference mechanism conforms the fuzzy inference system (FIS) that shapes the future conduct of the Center and the interrelationships between all variables. Integration into a single model of factors as diverse and yet highly interrelated with as participation, co-leadership and autonomy variable as "research & development willingness" and others like impacts, production, relevance and optimization to identify possible capacity variable analysis from a vision trans-disciplinary process of self-organization and school management.
Author Mr Di Chen : Ecole Polytechnique Féderale de Lausanne: Financial Engineering Section
This paper shows that complexity influences stock returns. By establishing the complexity and resilience measure of the common stock and analyzing the relationship between return, momentum, size, complexity, book-to-market ratio and resilience, three measures (size, complexity and momentum) stand out as the factors that can influence stock returns.
Cointegration of Interest Rate- The Case of Albaniarahulmonikasharma
In economic theories, the study of non-stationary time series, takes a special place. These series may have causal links between them in the short and medium term. In this paper, attention focuses on discovering links in the long term. If there are long-term bonds between the series then the series are said that cointegrate. Various tests, such as Engel-Granger's two-steps procedure, three steps Engle-Yoo method, Saikkonen method and Johansen method, will be analyzed. For each of these methods, advantages and disadvantages are given. In the last part of the paper, these methods are applied for real series such as the interest rate on credit and deposit interest ratesfor Albania.
Dimensionality Reduction Techniques In Response Surface Designsinventionjournals
Dimensionality reduction has enormous applications in various fields in industries. It can be applied in an optimal way with respect to time and cost related to the agricultural sciences, mechanical engineering, chemical technology, pharmaceutical sciences, clinical trials, biological studies, image processing, pattern recognitions etc. Several researchers made attempts on the reduction of the size of the model for different specific problems using some mathematical and statistical techniques identifying and eliminating some insignificant variables.This paper presents a review of the available literature on dimensionality reduction
A Critique of Factor Analysis of Interest RatesIlias Lekkos
Any old paper of mine on the limitations of applying factor analysis and principal component analysis on interest rates that has received renewed attention
ABSTRACT : This paper critically examined a broad view of Structural Equation Model (SEM) with a view
of pointing out direction on how researchers can employ this model to future researches, with specific focus on
several traditional multivariate procedures like factor analysis, discriminant analysis, path analysis. This study
employed a descriptive survey and historical research design. Data was computed viaDescriptive Statistics,
Correlation Coefficient, Reliability. The study concluded that Novice researchers must take care of assumptions
and concepts of Structure Equation Modeling, while building a model to check the proposed hypothesis. SEM is
more or less an evolving technique in the research, which is expanding to new fields. Moreover, it is providing
new insights to researchers for conducting longitudinal investigations.
.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Development of a Spatial Path-Analysis Method for Spatial Data AnalysisIJECEIAES
Path analysis is a method for identifying and analyzing direct and indirect relationship be- tween independent and dependent variables. This method was developed by Sewal Wright and initially only used correlation analysis results in identifying the variables’ relationship. So far, path analysis has been mostly used to deal with variables of non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to enhance path analysis to identify the effects of spatial dependencies. This paper proposes a method derived from path analysis that can process data with spatial elements and furthermore can be used to identify and analyze the spatial effects on the data; we call this method spatial path analysis.
PREDICTING CLASS-IMBALANCED BUSINESS RISK USING RESAMPLING, REGULARIZATION, A...IJMIT JOURNAL
We aim at developing and improving the imbalanced business risk modeling via jointly using proper
evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques.
Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison
based on 10-fold cross validation. Two undersampling strategies including random undersampling (RUS)
and cluster centroid undersampling (CCUS), as well as two oversampling methods including random
oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE), are applied. Three highly
interpretable classifiers, including logistic regression without regularization (LR), L1-regularized LR
(L1LR), and decision tree (DT) are implemented. Two ensembling techniques, including Bagging and
Boosting, are applied on the DT classifier for further model improvement. The results show that, Boosting
on DT by using the oversampled data containing 50% positives via SMOTE is the optimal model and it can
achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and 0.8907, respectively.
Information Economics Cost-Benefit Analysis on Automatic Billing System Imple...Trisnadi Wijaya
Information Economics Cost-Benefit Analysis
on Automatic Billing System Implementation
at Ogan Central Electronic
Authors:
Trisnadi Wijaya
Rika Kharlina Ekawati
Published at:
2014 International Conference on Economic and Information System Management Proceedings
ISBN : 978-602-71513-0-7
Proceedings URL : http://eprints.mdp.ac.id/1180/
SmartPLS is a software application for (graphical) path modeling with latent variables (LVP). The partial least squares (PLS)-method is used for the LVP-analysis in this software.
Berikut ini adalah slide contoh sewaktu saya presentasi proposal skripsi untuk kelulusan gelar sarjana saya. Sekedar berbagi untuk blog saya di http://arryrahmawan.net. Punya saran, tips, dan trik bagaimana membuat contoh slide presentasi proposal skripsi yang keren? Yuk mention Twitter saya di @ArryRahmawan
Space travel is dangerous and expensive, but it doesn't have to be. Find out about an alternative way to reach orbit that is rapidly becoming feasible and may eventually change how we view our world.
Multiple Linear Regression Model with Two Parameter Doubly Truncated New Symm...theijes
The most commonly used method to describe the relationship between response and independent variables is a linear model with Gaussian distributed errors. In practical components, the variables examined might not be mesokurtic and the populace values probably finitely limited. In this paper, we introduce a multiple linear regression models with two-parameter doubly truncated new symmetric distributed (DTNSD) errors for the first time. To estimate the model parameters we used the method of maximum likelihood (ML) and ordinary least squares (OLS). The model desires criteria such as Akaike information criteria (AIC) and Bayesian information criteria (BIC) for the models are used. A simulation study is performed to analysis the properties of the model parameters. A comparative study of doubly truncated new symmetric linear regression models on the Gaussian model showed that the proposed model gives good fit to the data sets for the error term follow DTNSD
Today’s market evolution and high volatility of business requirements put an increasing emphasis on the
ability for systems to accommodate the changes required by new organizational needs while maintaining
security objectives satisfiability. This is all the more true in case of collaboration and interoperability
between different organizations and thus between their information systems. Ontology mapping has been
used for interoperability and several mapping systems have evolved to support the same. Usual solutions
do not take care of security. That is almost all systems do a mapping of ontologies which are unsecured.
We have developed a system for mapping secured ontologies using graph similarity concept.
Generalized Instrumental Variables Estimation of Nonlinear Rat.docxbudbarber38650
Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models
Author(s): Lars Peter Hansen and Kenneth J. Singleton
Reviewed work(s):
Source: Econometrica, Vol. 50, No. 5 (Sep., 1982), pp. 1269-1286
Published by: The Econometric Society
Stable URL: http://www.jstor.org/stable/1911873 .
Accessed: 13/06/2012 01:46
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Econometrica, Vol. 50, No. 5 (September, 1982)
GENERALIZED INSTRUMENTAL VARIABLES ESTIMATION
OF NONLINEAR RATIONAL EXPECTATIONS MODELS'
BY LARS PETER HANSEN AND KENNETH J. SINGLETON
This paper describes a method for estimating and testing nonlinear rational expectations
models directly from stochastic Euler equations. The estimation procedure makes sample
counterparts to the population orthogonality conditions implied by the economic model
close to zero. An attractive feature of this method is that the parameters of the dynamic
objective functions of economic agents can be estimated without explicitly solving for the
stochastic equilibrium.
1. INTRODUCTION
THE ECONOMETRIC IMPLICATIONS of dynamic rational expectations models in
which economic agents are assumed to solve quadratic optimization problems,
subject to linear constraints, have been analyzed extensively in [11, 12, 25, 29,
and 30]. Linear-quadratic models lead to restrictions on systems of constant
coefficient linear difference equations, which provide complete characterizations
of the equilibrium time paths of the variables being studied. Hence, the parame-
ters of these models can be estimated using the rich body of time series
econometric tools developed for the estimation of restricted vector difference
equations [e.g., 11, 20, and 32]. Once the linear-quadratic framework is aban-
doned in favor of alternative nonquadratic objective functions, dynamic rational
expectations models typically do not yield representations for the variables that
are as convenient from the standpoint of econometric analysis. Indeed, in many
models, closed-form solutions for the equilibrium time paths of the variables of
interest have been obtained only after imposing strong assumptions on the
stochastic properties of the "forcing variables," the .
The tensor language provides a unifying approach that simplifies notation, which leads to compact modeling of multi-way information objects in many knowledge fields, and a thought framework as well. By such a language, it is modeled a generic system that connects to environment through its boundaries.
Analysis of Existing Models in Relation to the Problems of Mass Exchange betw...YogeshIJTSRD
The main recommendations of this article mainly analyzing the rate of harmful elements the period of exploitation of the automobile implements and its services to develop activity of automobile implements of the exploitation period. Shavkat Giyazov "Analysis of Existing Models in Relation to the Problems of Mass Exchange between Autotransport Complex and the Environment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38681.pdf Paper URL: https://www.ijtsrd.com/engineering/automotive-engineering/38681/analysis-of-existing-models-in-relation-to-the-problems-of-mass-exchange-between-autotransport-complex-and-the-environment/shavkat-giyazov
Decentralized data fusion approach is one in which features are extracted and processed individually and finally fused to obtain global estimates. The paper presents decentralized data fusion algorithm using factor analysis model. Factor analysis is a statistical method used to study the effect and interdependence of various factors within a system. The proposed algorithm fuses accelerometer and gyroscope data in an inertial measurement unit (IMU). Simulations are carried out on Matlab platform to illustrate the algorithm.
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.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact [email protected]
.
INFORMS is collaborating with JSTOR to digitize, preserve and extend access to Organization Science.
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All use subject to JSTOR Terms and Conditions
Complexity Theory and Organization Science
Philip Anderson
Amos Tuck School, Dartmouth College, Hanover New Hampshire 03755-9000
Abstract
Complex organizations exhibit surprising, nonlinear behavior.
Although organization scientists have studied complex organi-
zations for many years, a developing set of conceptual and com-
putational tools makes possible new approaches to modeling
nonlinear interactions within and between organizations. Com-
plex adaptive system models represent a genuinely new way of
simplifying the complex. They are characterized by four key
elements: agents with schemata, self-organizing networks sus-
tained by importing energy, coevolution to the edge of chaos,
and system evolution based on recombination. New types of
models that incorporate these elements will push organization
science forward by merging empirical observation with com-
putational agent-based simulation. Applying complex adaptive
systems models to strategic management leads to an emphasis
on building systems that can rapidly evolve effective adaptive
solutions. Strategic direction of complex organizations consists
of establishing and modifying environments within which ef-
fective, improvised, self-organized solutions can evolve. Man-
agers influence strategic behavior by altering the fitness land-
scape for local agents and reconfiguring the organizational
architecture within which agents adapt.
(Complexity Theory; Organizational Evolution; Strategic
Management)
Since the open-systems view of organizations began to
diffuse in the 1960s, comnplexity has been a central con-
struct in the vocabulary of organization scientists. Open
systems are open because they exchange resources with
the environment, and they are systems because they con-
sist of interconnected components that work together. In
his classic discussion of hierarchy in 1962, Simon defined
a complex s ...
A comparative study of clustering and biclustering of microarray dataijcsit
There are subsets of genes that have similar behavior under subsets of conditions, so we say that they
coexpress, but behave independently under other subsets of conditions. Discovering such coexpressions can
be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of
utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes
that are coexpressed under clusters of conditions. This type of clustering is called biclustering.
Biclustering is an NP-hard problem. Consequently, heuristic algorithms are typically used to approximate
this problem by finding suboptimal solutions. In this paper, we make a new survey on clustering and
biclustering of gene expression data, also called microarray data.
In order to solve the complex decision-making problems, there are many approaches and systems based on the fuzzy theory were proposed. In 1998, Smarandache introduced the concept of single-valued neutrosophic set as a complete development of fuzzy theory. In this paper, we research on the distance measure between single-valued neutrosophic sets based on the H-max measure of Ngan et al. [8]. The proposed measure is also a distance measure between picture fuzzy sets which was introduced by Cuong in 2013 [15]. Based on the proposed measure, an Adaptive Neuro Picture Fuzzy Inference System (ANPFIS) is built and applied to the decision making for the link states in interconnection networks. In experimental evaluation on the real datasets taken from the UPV (Universitat Politècnica de València) university, the performance of the proposed model is better than that of the related fuzzy methods.
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1. Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
Author(s): Michael E. SobelReviewed work(s): Source: Sociological Methodology, Vol. 13 (1982), pp. 290-312Published by: WileyStable URL: http://www.jstor.org/stable/270723 . Accessed: 11/02/2013 17:08Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp . JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact support@jstor.org. . Wiley and John Wiley & Sons are collaborating with JSTOR to digitize, preserve and extend access toSociological Methodology. http://www.jstor.org
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2. ASYMPTOTIC CONFIDENCE INTERVALS FOR INDIRECT EFFECTS IN STRUCTURAL EQUATION MODELS Michael E. Sobel INDIANA UNIVERSITY Since its general introduction to the sociological commu- nity (Duncan, 1966), path analysis has become one of the most widely used tools in sociological research, and through its use sociologists have become more sophisticated about the general For comments on an earlier draft of this chapter and for detailed ad- vice I am indebted to Robert M. Hauser, Halliman H. Winsborough, and Toni Richards, several anonymous reviewers, and the editor of this volume. I also wish to thank John Raisian, Nancy Rytina, and Barbara Mann for their comments and Mark Wilson for able research assistance. The opinions ex- pressed here are the sole responsibility of the author. 29()
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3. ASYMPTOTIC CONFIDENCE INTERVALS 291 topic of structural equation models. (For a thorough review of the literature, see Bielby and Hauser, 1977.) An intriguing aspect of path analysis, as traditionally propounded, is that "it makes explicit both the direct and the indirect effects of causal variables on dependent variables" (Duncan, Featherman, and Duncan, 1972, p. 23) and thereby allows for a detailed substantive accounting of the sociological process under investigation. Despite the enormous interest in indirect effects displayed by sociologists (Finney, 1972; Heise, 1975; Land, 1969; Lewis-Beck, 1974; Blalock, 1971; Duncan, Featherman, and Duncan, 1972; Blau and Duncan, 1967; Alwin and Hauser, 1975; Duncan, 1975), the distribution of these effects has been ignored. Thus sociologists (and re- searchers in many other disciplines as well) typically treat the indirect effects they calculate as parameter values and formu- late inferences without asking whether the effect itself is statisti- cally significant. I propose to remedy this situation by deriving the asymp- totic distribution of indirect effects and indicating, both for- mally and by example, how researchers may easily compute confidence intervals for their estimates. Since I assume that the reader is already well acquainted with the literature on indirect effects, I offer no extended discussion, per se, of this literature. Although I focus on recursive models, the results that are stated hold true for functions of the structural coefficients under quite general conditions. Since an indirect effect is de- fined as such a function, there is no need to proceed on a case by case basis. In the first section, I show how confidence intervals for the indirect effects of a recursive structural equation model may be obtained. In the second section, I work out the example in Alwin and Hauser (1975), which is taken from Duncan, Featherman, and Duncan (1972), and demonstrate the ease with which the confidence intervals are obtained. In addition, I show how inferences about the relative magnitudes of direct and indirect effects may be formulated. The results of this com- parison demonstrate the value of computing asymptotic confi-
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4. 292 MICHAEL E. SOBEL dence intervals and suggest that researchers proceed with cau- tion before offering detailed substantive explications of the indirect effects in complex structural models. THE FORMAL THEORY In a recursive structural equation model, the indirect ef- fects of an independent variable on a dependent variable may be expressed as a linear combination of products of structural parameters.1 Similarly, in a nonrecursive model or a model with latent variables the indirect effects may be expressed as a function of products of the structural parameters (Fox, 1980; Schmidt, 1980).2 In either case, the indirect effects may be regarded as a nonlinear function of the structural parameters. In order to place confidence intervals around the indi- rect effects, their distribution must first be determined. Among other things, this requires, for a given model, an assessment of the distribution of the complete coefficient vector under a par- ticular estimation procedure. The techniques that are generally used for estimating structural equation models are either maximum-likelihood procedures (for example, full-informa- tion maximum likelihood) or procedures that have similar asymptotic properties (such as three-stage least squares). The justification for considering these techniques derives largely from the fact that under general regularity conditions the com- plete coefficient vector is consistent, asymptotically normally distributed, and efficient (Theil, 1971, pp. 392-396). That is, whereas the small-sample properties of these estimators are often not well known, the large-sample properties are both attractive and tractable. For this reason, primarily, a general procedure for assessing the distribution of indirect effects will use asymptotic distribution theory as opposed to exact distribu- tion theory. In a recursive model there are no feedback relationships among the dependent variables and the disturbances are uncorrelated across equations. A more formal definition is presented later in the exposition. 2 This is true under the regularity conditions stated by Fox (1980, p. 18) and Schmidt (1980, pp. 9-10).
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5. ASYMPTOTIC CONFIDENCE INTERVALS 293 The provision of an asymptotic distribution theory for indirect effects requires two steps. First, the asymptotic distri- bution of the coefficient vector must be determined for a given structural equation model and a given estimation procedure. Second, because the indirect effects are a nonlinear function of the structural coefficients, an appropriate method for eval- uating the asymptotic distribution of such functions must be employed. The multivariate-delta method is utilized for this purpose: It provides a simple, general, and elegant means for evaluating the asymptotic distribution of functions of multinor- mally distributed random variables. With these results in hand, and with a consistent estimator of the variances of the indirect effects, we find it easy to construct a (1 - a)100% confidence interval for the indirect effect under consideration. Asymptotic Distribution of the Coefficient Vector in a Recursive Model We begin by considering a system of M equations. For the jth structural equation, j = 1, . . . , M, let the model be Yi = Z,S, + E, (1) where yj is the vector of dependent random variables for the n observations and Ej is a stochastic term distributed N(O, o-2I); moreover, Zj = (Xj, Yj) is the full-rank matrix of predeter- mined and endogenous variables, respectively, and 6j is the cor- responding parameter vector (7*, f3j*)'. Combining the M equations, we may rewrite the system in the alternative form YB= xr+ E (2) where Y = (y1, , YM), X=(X1, . . ., XK) B = (f81h . . ., /3M) is the nonsingular matrix of coefficients of the endogenous variables, r = (Vi, . . .y, m) is the K x M matrix of coefficients for the exogenous variables, and E = (El, . * *,E(M). We assume that the restrictions on r include only zero restrictions, whereas for B the restrictions include zero restrictions as well as the restrictions that the diagonal ele- ments are set to 1. Finally, we assume that each row of E is inde- pendently and identically distributed N(O', I).
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6. 294 MICHAEL E. SOBEL System (2), with the assumptions stated here, specifies the complete form of the structural equation model under con- sideration. If the model is identified, the unrestricted elements 6 = (6,. . . , aM)in the parameter matrices B and r may be estimated by means of maximum likelihood (Rothenberg and Leenders, 1964; Theil, 1971; J6reskog, 1973). Under general regularity conditions (Theil, 1971, p. 395), this estimator has, in large samples, an approximately normal distribution with mean 6 and variance-covariance matrix n-1[I(6)]-1, where n is the size of the sample and [1(6)] is the Fisher information matrix (Rao, 1973, pp. 324-328; Theil, 1971, pp. 384-396). The precise statement is that n1I2(6a - 8) A N(O, [1(6)]1) (3) That is, n1/2(6n -8) converges in law (distribution) to the quan- tity on the right-hand side of (3), where 6n is the maximum- likelihood estimator of 6 based on a sample of size n. In the special case where B is an upper-triangular matrix and t is diag- onal, the system (2) is recursive (Theil, 1971, p. 461). It is well known that for this case (Rothenberg and Leenders, 1964; Ma- linvaud, 1970; Theil, 1971) maximum-likelihood estimation of the system reduces to equation-by-equation least-squares esti- mation because the log-likelihood function is merely the sum of the M likelihood functions for the structural relationships spe- cified by (1). That is, the log-likelihood function for the system (2) is given by c - (n/2) log I (B') 1lB-1 M M 2) E E 'M(ym - Zm'6m')' (Y M Zm6m) m'=1 M=1 which reduces to M L(81m , M, ) = c + (n/2) E log AP-' j=1 (4) M -2() E (Yj -ZjSj) f (Yj - ZjSj)4rj- j=1 in the recursive case. For this case, it may also be shown (see
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7. ASYMPTOTIC CONFIDENCE INTERVALS 295 Appendix) that [1(8)]-1 is a block-diagonal matrix Q-1 0 0 . . . 0 O Q21 0 0 o o Q'1 0 o o o . . . Q-1 with components Qf1 = plim Tjj(Z;zj/n)-1.3 That is, for a re- cursive model the vector an obtained by combining the M least-squares estimators satisfies n1/2(& -6) N(O, [Q(8)]-1) (5) Hence an is asymptotically normally distributed. Now that the asymptotic distribution of the coefficient vector has been established, it is necessary to consider the asymptotic distribution of the indirect effects. As previously in- dicated, the multivariate-delta method is used for this purpose. Because this technique may be unfamiliar to some readers, a description of the procedure follows. Readers who are familiar with this technique may wish to skip the exposition and go directly to the results. The Delta Method The multivariate-delta method (Rao, 1973, pp. 385- 389; Bishop, Fienberg, and Holland, 1975, pp. 486-500) pro- vides a general method for establishing the asymptotic dis- tribution of a differentiable vector function of a multinormally distributed random vector. The method is an extension of the single-delta method, which is justified by a theorem which states that if a random variable 6n satisfies 1n 2(8 - 6) -4 N(0 2(6)) (6) 3 Note that Tjj and ZjZj both depend on n. Because it is cumbersome to subscript all the random variables, the n subscript is omitted when there is no risk of confusion.
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8. 296 MICHAEL E. SOBEL then any functionf that is differentiable in a neighborhood of 8 will satisfy n112(f(6n) -f(8)) N(O, [fP(8)]2 v2(8)) (7) providedf'(8) does not vanish (Rao, 1973, pp. 386-387). For example, in a single-variable regression model y - = b(x - x) + e with conditional variance o-2 and least-squares estimator b, the asymptotic distribution of (b)-1 may be of interest. By (5) we know that n 2(bn_-b) -$N(O, 2 lim((x -)2/n)-) and application of (7) yields the conclusion that4 n n (b-l-b1) -3 N(O, (-b2)2 2 lim((x /n)- ) Note, however, that as b approaches zero, (b)-1 becomes un- bounded and the delta method cannot be applied. To see heuristically why the single-delta method works, we consider a Taylor expansion off, a function of the parame- ter set. Suppose thatf is twice differentiable in a neighborhood of 6. A Taylor expansion off about 8 is given by f(6M) -f(8) = f '(8) (8n - 6) + f "(6*) (8n - 6)2 /2 (8) where 6* is some number in the interval (On, 6). For large n, 6* is close to 6, so that the first term on the right-hand side of (8) dominates the second, which may be considered negligible. Thus f(6n) -f(8) f '(8) (6n - 6) (9) That is, f(68)-f(6) is an approximately linear function of An. Because An is approximately normal in large samples and linear functions of normally distributed random variables are nor- mally distributed,f(6k) has an approximate normal distribution 4 For this result to hold, it is technically necessary to assume the exist- ence of the limit in question (Theil, 1971, p. 363).
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9. ASYMPTOTIC CONFIDENCE INTERVALS 297 in large samples. Taking expectations and variances of both sides of (9), we see that E(f (8n) =:- f (8) and V( f ( ))- n -l [ft(8)]1V2(8) That is, for large n the result (7) holds. To consider the multivariate-delta method, let f be a function that is differentiable in a neighborhood of an S- dimensional vector 8 and let (afasyt = (af/dl1' . . , df/d6s) If n1/2(- 8) A N(O,1(8)) (10) then n12 (f(-n) -f(8)) N(O, (Of/6s)'(8)(Of/Os)) (11) provided the quadratic form (c6f/68)' I() (cf/68) does not vanish (Rao, 1973, p. 387). Heuristically, this result may be justified by an extension of the argument used to establish the plausibility of the single-delta method. Suppose thatf has the Taylor expansion f(8) -f (8) = (8n - 8)' (Of/O8) + Rn (12) where Rn approaches zero as n increases without bound. Then, for n large, f8(6) - f(8) - (8n 8)' (Cf/CO) (13) This is a linear function of a multinormally distributed random vector; hence its distribution is normal, E(f(Ak) =f(8) and V(f8({n)) ==n-1 [(a*f/a*)8) 1(8) (c*f/c*)8) As an example, consider the two-variable regression model y - b(x - x) + c(z - z) + e
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10. 298 MICHAEL E. SOBEL and suppose that the product bc is of interest. Let bn = (bn, en)' and let b = (b, c)'. Suppose that nl 2(bn - b) N (?,( 2 j 22)) (14) Application of the multivariate-delta method then yields the conclusion that n12 (bc - bc) -* N(O, C2o-11 + 2bcou12 + b2o-22) (15) Note, however, that if both b and c are zero the variance term vanishes and the initial conditions for application of the delta method are not met. Obtaining Asymptotic Confidence Intervals for the Indirect Effects To obtain asymptotic confidence intervals for the indi- rect effects, their asymptotic distribution must first be deter- mined; a general form of the multivariate-delta method is used to obtain this result. Let F = (fi, . . f')' be a vector-valued function that is differentiable in a neighborhood of an S- dimensional parameter vector 8, and let (OF/06) be the matrix Qfi /a81 Jaf, /8 If n2(6n - 6) A N(0,1(8)) (16) then a differentiable function F satisfies n (F(Sn) - F(6)) A N(0, (OF/68)1(6)(OF/O8)') (17) where the rank of the covariance matrix in (17) is less than or equal to I (Rao, 1973, p. 388). Obtaining the asymptotic distribution of the indirect ef- fects in a recursive model is now a simple matter. Let F-
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11. ASYMPTOTIC CONFIDENCE INTERVALS 299 (f1, . . , f,)' now be a column vector of indirect effects. Be- cause the fi are sums of products of the structural parameters, they are differentiable in a neighborhood of S. Thus results (5) and (17) may be combined, yielding the conclusion that A CD n 1/2 (F( - F(6)) A N(O, (OF/06)[Q(6)]-1(OF/068)') (18) That is, for large n the indirect effects in a recursive model have the approximate distribution N(F(8), n-1(OF/06) [Q(6)]-1(OF/06)) (19) To obtain asymptotic (1 - a)100% confidence intervals for the components of F, let Z N(0, 1) and let F(z*) = 1 - a/2, where cF is the distribution function of Z. Then, because the elements in aF/la and [Q(S)]-l are continuous near 8, a (1 - a)100% confidence interval for the ith component of F, i= 1, . . ., I, is given byf1(6) + wh/2z*; wii is the ith diagonal element of n-1(OF/06) [Q())]-1(aF/a6)I where 6 is used to indicate that the matrices are evaluated at the solution 8 (Rao, 1973, pp. 388-389).5 5 It should now be clear how to extend the results beyond the recursive case. In the more general case, the log-likelihood function for system (2) no longer takes form (4) but may be written as M M c - (n/2) log I (B')-14B-1 l -(1) E E -mm(YM, -Zm'1m')' (YM-zmam) m'=l m=1 where pm'm is the (m'm)th element of I-1 (Theil, 1971, p. 525). If W is not diagonal, maximization of the likelihood function with respect to the ele- ments of B, F, and I leads to the full-information maximum-likelihood esti- mator (J6reskog, 1973; Rothenberg and Leenders, 1964), which is asymptoti- cally equivalent to the three-stage least-squares estimator (Theil, 1971, p. 526; Sargan, 1964). If W is diagonal, maximization of the likelihood function with respect to the elements of B, r, and I yields the limited-information maximum-likelihood estimator, which is asymptotically equivalent to the two-stage least-squares estimator (Theil, 1971, p. 507). Thus either (1) three-stage least squares or full-information maximum likelihood or (2) two- stage least squares or limited-information maximum likelihood may be used to obtain the estimates and the inverted information matrix of Equation (3). (For mathematical expressions for the elements in the information matrix,
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12. 300 MICHAEL E. SOBEL AN EXAMPLE To illustrate how asymptotic confidence intervals for indirect effects may be obtained, the example from Alwin and Hauser (1975), originally taken from the correlation matrix in Duncan, Featherman, and Duncan (1972, p. 38), is reworked. See Figure 1 for the path diagram. The variables in this model of the achievement process are father's occupational status (Xa), father's education (Xb), number of siblings in the family of ori- Figure 1. Path diagram for a model of socioeconomic achievement. /3 ?2 see J6reskog, 1973, p. 112; Theil, 1971, p. 526; and Rothenberg and Leenders, 1964, p. 67.) Provided the indirect effects are differentiable, confi- dence intervals may be obtained as before. For models with unobserved variables, LISREL may be used to obtain the maximum-likelihood estimates and the inverse oif the information matrix. (See J6reskog, 1973, p. 109, for formulas.) Provided the indirect effects are differentiable, confidence intervals may be obtained as before. (On the sub- ject of indirect effects in models with unobserved variables, see Schmidt, 1980.)
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13. ASYMPTOTIC CONFIDENCE INTERVALS 301 entation (Xc), responde~nt's education (yl), respondent's occupa- tional status in March 1962 (y2), and respondent's income, ex- pressed in units of thousands, in 1961 (Ye). The results apply to nonblack men with nonfarm background in the experienced civilian labor force, of age 35 to 44 in March 1962.6 Table 1 presents the coefficients for the structural model and Table 2 displays the estimated indirect effects in both sym- bolic and numerical form.7 In addition, Table 2 contains the asymptotic standard errors of the indirect effects. The standard errors in Table 2 were obtained as follows. First thefi, i = 1, . . ., 10, are differentiated with respect to the structural parameters, yielding the 10 x 12 matrix (OF/06), with (is)th element (af/1a8s), evaluated at the solution 6. In symbolic form, Table 3 presents this matrix of partial derivatives; the parameter at the top of each column indicates that the partial derivative is taken with respect to that parame- ter. Next the estimated variance-covariance matrix of 8 is pre- multiplied by the matrix of Table 3 and postmultiplied by its transpose, yielding the estimated asymptotic variance-covar- iance matrix of the indirect effects (Table 4).8 From here it 6 All the results are presented for the unstandardized form of the model. However, the results that have been established can be modified to hold asymptotically for the standardized solution. 7 Readers of the Alwin and Hauser paper may wonder why the simpler expressions for the indirect effects they use are not employed here. Their ex- pressions are a function of both structural and reduced-form parameters; in general, to work with these expressions in a distributional framework one needs the joint distribution of the reduced-form parameters and the structu- ral parameters. Furthermore, the formulas given by Alwin and Hauser are valid for only a limited class of recursive models, as the authors note. Similarly, the expressions given by Fox (1980, p. 11) are not used be- cause the indirect effects he defines are "total" indirect effects-that is, the indirect effect of a variable on a subsequent variable is given as the sum of the indirect effects through all the intervening variables and combinations of the intervening variables. Here I prefer to allow the possibility that users may also find it worthwhile to examine particular components of "total" indirect ef- fects. See, for example, the illustration in this section.) 8 The estimated asymptotic covariance matrix is 12 x 12, as opposed to 15 x 15, because the variables in the analysis were, without loss of general- ity, deviated about their means. This is also the reason why the matrix of par- tial derivatives is 10 x 12 rather than 10 x 15.
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14. 302 MICHAEL E. SOBEL TABLE 1 COEFFICIENTS AND STANDARD ERRORS (IN PARENTHESES) FOR THE STRUCTURAL EQUATION MODEL OF SOCIOECONOMIC ACHIEVEMENT IN FIGURE 1 Dependent Variable Independent Variable Yi Y2 Y3 Xa 8ia = 0.0385 82a = 0.1352 83a = 0.0114 (0.0025) (0.0175) (0.0045) Xb 81b - 0.1707 82b = 0.0490 83b = 0.0712 (0.0156) (0.1082) (0.0275) Xc 81C = -0.2281 82c = -0.4631 83c = -0.0373 (0.0176) (0.1231) (0.0314) Yi 821 = 4.3767 831 = 0.1998 (0.1202) (0.0364) Y2 832 = 0.0704 (0.0045) NOTE: The sample size is 3,214; the population is nonblack, nonfarm, U.S. men in the experienced civilian labor force, aged 35-44 in March 1962. Symbols are as described in the text and Figure 1. The analysis is based on the correlation matrix and the standard deviations reported by Duncan, Featherman, and Duncan (1972, p. 38). TABLE 2 INDIRECT EFFECTS AND ASYMPTOTIC STANDARD ERRORS (IN PARENTHESES) FOR THE STRUCTURAL EQUATION MODEL OF FIGURE 1 Indirect Effects Indirect Effect Through of In- Dependent dependent Variable Variables Yi Alone Y2 Y2 Xa 11 = 82181a = 0.1685 (0.0118) Xb f2 = 82181b = 0.7471 (0.0712) Xc f3 = 82181c = -0.9983 (0.0818) Y3 Xa f4 = 8318ia = 0.0077 f7 = 832(82a + 82181a) = 0.0214 (0.0015) (0.0020) Xb f5 83181b = 0.0341 f8 = 832(82b + 82181b) = 0.0560 (0.0069) (0.0096) Xc 16 = 83181c = -0.0456 fA = 832(82c + 82181c) = -0.1028 (0.0090) (0.0111) Yi il = 832821 = 0.3081 (0.0214)
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15. ASYMPTOTIC CONFIDENCE INTERVALS 303 TABLE 3 SYMBOLIC FORM FOR aF/la 81a 81b 81c 82a 82b 82c 821 83a 83b 83c 831 832 f1 821 0 0 0 0 0 8ia 0 0 0 0 0 f2 0 821 0 0 0 0 81b 0 0 0 0 0 f3 0 0 821 0 0 0 8lc 0 0?0 0 0 f4 831 0 0 0 0 0 0 0 0 0 8ia 0 f5 0 831 0 0 0 0 0 0 0 0 81b 0 f6 0 0 831 0 0 0 0 0 0 0 8lC 0 f7 832821 0 0 832 0 0 83281a 0 0 0 0 82a + 82181a f8 0 832821 0 0 832 0 83281b 0 0 0 0 82b + 82181b f9 0 0 832821 0 0 832 83281C 0 0 0 0 82C + 82181C f1o 0 0 0 0 0 0 832 0 0 0 0 821 is a simple matter to pick out the diagonal elements and com- pute asymptotic standard errors, using these to create the (1 - a)100% confidence intervals. Table 2 indicates that while several of the indirect effects are small, in no case would a 95 or 99 percent confidence in- terval for any indirect effect cover the value zero.9 Hence it may be assumed that the indirect effects are nonzero. Thus, although the direct effect of father's education on respondent's occupational status is insignificant, the indirect effect on occu- pational status through respondent's education is greater than zero. In other words, educational advantages in the family of orientation favorably influence occupational status by aug- menting respondent's education, which in turn has a positive direct effect on occupational status. Similarly, although the direct effect of the sibling variable on income is not statistically different from zero, increasing the number of siblings in the family of orientation decreases income both by decreasing re- spondent's educational attainment (f6) and by decreasing re- spondent's educational attainment and respondent's occupa- tional status (fe). It is often of considerable theoretical and pragmatic value to compare the relative magnitudes of appropriate direct 9 Alternatively, we could say, with 90 percent confidence, that none of the indirect effects, considered jointly, are zero (Miller, 1966).
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16. TABLE 4 ASYMPTOTIC VARIANCE-COVARIANCE MATRIX (MULTIPLIED A f2 f3 A A f, fi 1.3912 f2 -2.7139 50.6970 fA -0.2800 4.3676 66.9629 f4 0.0538 -0.1671 0.0449 0.0220 fA -0.1671 2.1225 0.4554 0.0792 0.4825 Ae 0.0449 0.4554 2.7148 -0.1140 -0.4942 0.8119 f7 0.0830 -0.2579 0.0695 -0.0058 -0.0545 0.0603 0.0385 A -0.2579 3.2740 0.7037 -0.0371 0.0373 0.1819 -0.0134 Ag 0.0695 0.7039 4.1868 0.0363 0.1792 -0.0053 -0.0472 fio 0.3902 1.7322 -2.3137 -0.1389 -0.6166 0.8236 0.2677 This content downloaded on Mon, 11 Feb 2013 17:08:42 PM
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17. ASYMPTOTIC CONFIDENCE INTERVALS 305 and indirect effects (Alwin and Hauser, 1975; Duncan, Feath- erman, and Duncan, 1972). There are a variety of ways in which such comparisons may be made; for example, one could assess the proportion of the total effect that is direct (indirect). Alternatively, the ratio of the direct (indirect) effect of interest to the relevant indirect (direct) effect may be assessed, yielding information on both the relative magnitude and direction of influence of the direct (indirect) effects.10 To implement such comparisons, we define the ratios ri, i = 1, . . . , I, as gi(6)/fi(6), where fi(6) is as previously defined and gi(6) is the direct effect of the appropriate independent variable on the dependent variable under consideration. Provided fi(6) # 0, the ri are differentiable and the multivariate-delta method may be used to obtain asymptotic confidence intervals for the ratios of interest. For example, let r1 = 82a/fi(6). From Table 2 it is clear that the null hypothesisf1(6) = 0 can be rejected at the 0.01 level; thus r1 may be assumed to be differentiable. Dif- ferentiating r1 with respect to the elements in 8 yields the 1 x 12 row vector with arl/a8la = -82a/82181a ari/a62a = (82181a)-i arl/a821 = -82a/81a 21 and all other partials equal to zero. Premultiplication of this row vector into the estimated asymptotic variance-covariance matrix of the structural coefficients and postmultiplication by 10 Alternatively, one could examine differences between direct and indirect effects. In general, the manner in which the comparisons are defined should depend both on substantive context and statistical tractability. To appreciate the latter point, suppose that we had defined the ri, i = 1, . . . , I, as the ratio of the indirect effect to the direct effect. Since the null hypotheses that 82b = O and 83e = 0 have not been rejected at the 0.05 level (see Table 1), it is not reasonable to allow the comparisons that use these expressions in the denominator: If 82b and 83c are zero, the differentiability conditions are vio- lated. However, because it has been established that the indirect effects may be assumed nonzero, it is permissible to formulate all comparisons that use these expressions in the denominator of the ratios, as we have done. Of course, when the direct effect is zero, the ratio of the direct to the indirect ef- fect should not deviate significantly from zero.
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18. 306 MICHAEL E. SOBEL the transpose of the row vector yields the estimated asymptotic variance of rf. It is now a simple matter to construct (1 - a)I 00% confidence intervals for the ri, i = 1, . . . , I. Table 5 presents values of the ri and their asymptotic standard errors for i = 1, . . , 10; Table 6 presents 95 and 99 percent confidence intervals for the ri. Moreover, Table 6 presents confidence intervals for three additional functions. First, the ratio of the direct effect 63a to the sum of the indirect effectsf4 andf7 is defined as r1,. Clearly r11 is simply the ratio of the direct effect of Xa on y3 to the "total" indirect effect of Xa on y3-that is, the indirect effect that operates jointly through Yi alone, through Y2 alone, and through Yi and Y2* Similarly, r12 is defined as 63b/f5 + f8 and r13 is defined as 83c/f6 + f9. Inspection of Table 6 reveals that r2, r6, r9, and r13 may be assumed to equal zero; this result recapitulates the earlier observation that 62b and 83c are not statistically different from zero. In addition, the null hypotheses that r3 and r1l are less than 1 are not rejected, at either the 0.05 or 0.01 level, on the TABLE 5 RATIOS OF DIRECT TO INDIRECT EFFECTS AND ASYMPTOTIC STANDARD ERRORS (IN PARENTHESES) FOR THE STRUCTURAL EQUATION MODEL OF FIGURE 1 Indirect Effects De- of Inde- Indirect Effect Through pendent pendent Variable Variables Yi Alone Y2 Y2 Xa il = 82a/82181a = 0.8024 (0.1231) Xb f2 = 82b/82181b = 0.0656 (0.1452) Xc r3 = 82c/82181c = 0.4639 (0.1318) Y3 Xa r4 = 83a/83181a = 1.4820 r7 = 83a/832(82a + 82181a) = 0.5332 (0.6860) (0.2205) Xb r5 = 83b/83181b = 2.0876 r8 = 83b/832(82b + 82181b) = 1.2704 (0.9631) (0.5376) Xc rS = 83c/83181c = 0.8184 rs = 83c/832(82c + 82181c) = 0.3625 (0.7298) (0.3098) Yi r10 = 831/832821 = 0.6485 (0.1451)
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19. ASYMPTOTIC CONFIDENCE INTERVALS 307 TABLE 6 ASYMPTOTIC CONFIDENCE INTERVALS FOR RATIOS OF DIRECT TO INDIRECT EFFECTS IN A MODEL OF SOCIOECONOMIC ACHIEVEMENT 95% CI 99% CI (0.5827,1.0221) (0.4848,1.1200) r2 (-0.2190,0.3502) (-0.3090,0.4402) r3 (0.2056,0.7222) (0.1239,0.8039) r4 (0.1374,2.8266) (-0.2879,3.2519) r5 (0.1999,3.9753) (-0.4839,4.5724) r8 (-0.6120,2.2488) (-1.0645,1.8828) r7 (0.1010,0.9654) (-0.0357,1.1021) r8 (0.2167,2.3241) (-0.1166,2.6574) r9 (-0.2447,0.9697) (-0.4368,1.2164) rio (0.3641,0.9329) (0.2741,1.0229) rll (0.0729,0.7107) (-0.0280,0.8116) r12 (0.1407,1.4397) (-0.0648,1.6452) r13 (-0.1723,0.6745) (-0.3062,0.8084) basis of these data. Thus the indirect effect of the sibling vari- able on respondent's occupational status is more negative than the direct effect, indicating that additional siblings in the family of orientation detract from subsequent occupational attain- ments predominantly by attenuating the educational experi- ences of individuals reared in larger families. Similarly, the ef- fect of father's occupational status on respondent's income is predominantly indirect: Respondent's income is enhanced by high-status origins, primarily because high-status origins incre- ment subsequent educational and occupational achievements. The widths of the confidence intervals do not appear to allow strong inferences about the magnitudes of the other com- parisons in Table 6. Nevertheless, it is not unreasonable to suggest that rl, r7, and r,0 are not greater than 1. In other words, the direct effects of father's status on son's status and in- come are not larger than the effects that intervene through son's education and son's occupational status, respectively; sim- ilarly, the direct effect of respondent's education on income is not larger than the indirect effect of education on income. On balance, the analysis suggests that the indirect effects in the model under consideration are nonzero, but the confi- dence intervals for the ratios in Table 6 are too large to permit
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20. 308 MICHAEL E. SOBEL precise inferences about the relative magnitudes of the direct and indirect effects. In turn, the example illustrates the utility of computing the confidence intervals and suggests that re- searchers proceed with both caution and thoughtfulness before offering detailed substantive interpretations of the various ef- fects in complex causal models. SUMMARY In the preceding pages I have proposed a method for as- sessing the significance of indirect effects in structural equation models and given an example. The method itself is quite simple, and it is easy to perform the necessary computations. All one needs is a regression program that computes the variance-covariance matrix of the coefficients (for example, BMDP or LISREL), a calculator for computing the elements of the matrix of partial derivatives, and a matrix multiplication program for computing the estimated asymptotic variance- covariance matrix of the indirect effects. Alternatively, the computations can be performed in one step with a good matrix algebra program (for example, PROC MATRIX in SAS). One caveat: The confidence intervals derived here are valid for large samples. Since one seldom knows when a sample is large enough, the application of these methods may be inap- propriate in particularly small samples. APPENDIX In this appendix we derive the asymptotic distribution of the complete coefficient vector in a recursive model. We begin with the log-likelihood function M , . , =M, c)=C + (n/2) E log I 3=1 (A-1) M - (2) E (yj - Zf3j)' (y- Z_f36) .-1 j=1 which is identical to (4) in the text.
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21. ASYMPTOTIC CONFIDENCE INTERVALS 309 Following Rothenberg and Leenders (1964, pp. 60-61), we differentiate (A- 1) with respect to Tjj, j = 1, . . . , M, ob- taining the conclusion that in order for (A-1) to be maximized, it must be the case that XIjj = n-1(yj - ZjSj)' (yj - Zj8j) = TX' j = 1, . . . , M Substitution of this result into (A-1) yields the concentrated log-likelihood function M L(81, m, &) = C - (n/2) I log TI' (A-2) j=1 which may be maximized in lieu of (A-i).11 Differentiating (A-2) with respect to S1, . . , Sm yields the first-order deriva- tives aL/,8j = P (z;(Y, - ZSj,)) j = 1, . . ., M (A-3) and repeated differentiation yields O if j7h a2L/OSjOSh - T-'Zzj + 2(nTX.)-1(Z;(yj - Zj8j) (A-4) (yj - Zj6j)'Zj) = Gj ifj h Let S = (Si, . . . , t) ' as in the text; then combining the ele- ments of (A-4) gives -G, . . . o~0 0 G2 0 a2L/ 6O6 _ 0 0 G3 0 0 0 0 . GM It is well known that, under suitable regularity conditions, plim(n-1 (C2L/O')SO') )) = -I(S) where I is the Fisher information matrix (Rao, 1973, p. 366). "1 In effect we are solving for a subset of the parameters and using the solution to simplify the maximization problem. For a complete treatment of concentrated likelihood functions, see Dhrymes (1974, pp. 324-334).
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22. 310 MICHAEL E. SOBEL Suppose that T-1l(Z;Zj/n) has plim Qj X 0, j = 1, . . ., M. Since plim n-1 (Z'(yj - ZjSj)) = 0 from (A-4) we readily obtain Qi o o 0 0 o Q2 0 0 o 0 Q3 0 o 0 ? . . .0 m Next we use the fact that under general regularity conditions (Theil, 1971, p. 395) nh/2( - 6)A N(O, [I(8)]-1) (A-5) That is, n1/2( , - 6) converges in law (distribution) to the quan- tity on the right-hand side of (A-5), where [I(8)]-1 is the block-diagonal matrix Qi1 0 0 0 0 Q21 0 0 o o Q-1 0 o o ~o . . . Q-1 0 0 0 tMe This suffices to show the result. REFERENCES ALWIN, D. F., AND HAUSER, R. M. 1975 "The decomposition of effects in path analysis." American Sociological Review 40:37-47. BIELBY, W. T., AND HAUSER, R. M. 1977 "Structural equation models." Annual Review of Sociology 3:137-161.
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23. ASYMPTOTIC CONFIDENCE INTERVALS 311 BISHOP, Y. M., FIENBERG, S. E., AND HOLLAND, P. W. 1975 Discrete Multivariate Analysis: Theory and Practice. Cam- bridge, Mass.: M.I.T. Press. BLALOCK, H. M. (ED.) 1971 Causal Models in the Social Sciences. Chicago: Aldine. BLAU, P. M., AND DUNCAN, 0. D. 1967 The American Occupational Structure. New York: Wiley. DHRYMES, P. J. 1974 Econometrics. New York: Springer-Verlag. DUNCAN, 0. D. 1966 "Path analysis: sociological examples." American Journal of Sociology 72:1-16. 1975 Introduction to Structural Equation Models. New York: Aca- demic Press. DUNCAN, 0. D., FEATHERMAN, D. L., AND DUNCAN, B. 1972 Socioeconomic Background and Achievement. New York: Sem- inar Press. FINNEY, J. M. 1972 "Indirect effects in path analysis." Sociological Methods and Research 1:175-186. FOX, J. 1980 "Effect analysis in structural equation models." Sociological Methods and Research 9:3-28. HEISE, D. R. 1975 Causal Analysis. New York: Wiley. JORESKOG, K. G. 1973 "A general method for estimating a linear structural equa- tion system." In A. S. Goldberger and 0. D. Duncan (eds.), Structural Equation Models in the Social Sciences. New York: Academic Press. LAND, K. C. 1969 "Principles of path analysis." In E. F. Borgatta (ed.), Socio- logical Methodology 1969. San Francisco: Jossey-Bass. LEWIS-BECK, M. S. 1974 "Determining the importance of an independent variable: A path analytic solution." Social Science Research 3:95- 108. MALINVAUD, E. 1970 Statistical Methods of Econometrics. Amsterdam: North- Holland. MILLER, R. 1966 Simultaneous Statistical Inference. New York: McGraw-Hill.
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24. 312 MICHAEL E. SOBEL RAO, C. R. 1973 Linear Statistical Inference and Its Applications. New York: Wiley. ROTHENBERG, T. J., AND LEENDERS, C. T. 1964 "Efficient estimation of simultaneous equation systems." Econometrica 32:57-76. SARGAN, J. D. 1964 "Three-stage least-squares and full maximum-likelihood estimates." Econometrica 32:77-81. SCHMIDT, P. 1980 "On decomposition of effects in causal models." Mann- heim: Zentrum fur Umfragen, Methoden und Analysen. Unpublished manuscript. THEIL, H. 1971 Principles of Econometrics. New York: Wiley.
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