This document appears to be the table of contents for a textbook on statistical inference and econometrics. It lists 13 chapters that cover topics such as basic statistical theory, experimental derivation of sampling distributions, probability and probability distributions, tests of hypotheses, estimation, simple regression, violations of assumptions, multiple regression, formulation and estimation of special models, simultaneous equation systems, and generalized linear regression models and applications. It also includes appendices on algebra, matrix algebra, asymptotic distributions, and statistical tables.
Bayesian system reliability and availability analysis underthe vague environm...ijsc
Reliability modeling is the most important discipline of reliable engineering. The main purpose of this paper is to provide a methodology for discussing the vague environment. Actually we discuss on Bayesian system reliability and availability analysis on the vague environment based on Exponential distribution
under squared error symmetric and precautionary asymmetric loss functions. In order to apply the Bayesian approach, model parameters are assumed to be vague random variables with vague prior distributions. This approach will be used to create the vague Bayes estimate of system reliability and availability by introducing and applying a theorem called “Resolution Identity” for vague sets. For this purpose, the original problem is transformed into a nonlinear programming problem which is then divided up into eight subproblems to simplify computations. Finally, the results obtained for the subproblems can
be used to determine the membership functions of the vague Bayes estimate of system reliability. Finally, the sub problems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.
ANALYTICAL FORMULATIONS FOR THE LEVEL BASED WEIGHTED AVERAGE VALUE OF DISCRET...ijsc
In fuzzy decision-making processes based on linguistic information, operations on discrete fuzzy numbers
are commonly performed. Aggregation and defuzzification operations are some of these often used
operations. Many aggregation and defuzzification operators produce results independent to the decisionmaker’s
strategy. On the other hand, the Weighted Average Based on Levels (WABL) approach can take
into account the level weights and the decision maker's "optimism" strategy. This gives flexibility to the
WABL operator and, through machine learning, can be trained in the direction of the decision maker's
strategy, producing more satisfactory results for the decision maker. However, in order to determine the
WABL value, it is necessary to calculate some integrals. In this study, the concept of WABL for discrete
trapezoidal fuzzy numbers is investigated, and analytical formulas have been proven to facilitate the
calculation of WABL value for these fuzzy numbers. Trapezoidal and their special form, triangular fuzzy
numbers, are the most commonly used fuzzy number types in fuzzy modeling, so in this study, such numbers
have been studied. Computational examples explaining the theoretical results have been performed.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Comparison of Bayesian and non-Bayesian estimations for Type-II censored Gen...IqraHussain31
Conference Research Article
Presented By
Iqra Sardar
16th International Conference on Statistical Sciences:
At Department of Statistics
Islamia College, Peshawar Khyber Pakhtunkhwa, Pakistan
Bayesian system reliability and availability analysis underthe vague environm...ijsc
Reliability modeling is the most important discipline of reliable engineering. The main purpose of this paper is to provide a methodology for discussing the vague environment. Actually we discuss on Bayesian system reliability and availability analysis on the vague environment based on Exponential distribution
under squared error symmetric and precautionary asymmetric loss functions. In order to apply the Bayesian approach, model parameters are assumed to be vague random variables with vague prior distributions. This approach will be used to create the vague Bayes estimate of system reliability and availability by introducing and applying a theorem called “Resolution Identity” for vague sets. For this purpose, the original problem is transformed into a nonlinear programming problem which is then divided up into eight subproblems to simplify computations. Finally, the results obtained for the subproblems can
be used to determine the membership functions of the vague Bayes estimate of system reliability. Finally, the sub problems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.
ANALYTICAL FORMULATIONS FOR THE LEVEL BASED WEIGHTED AVERAGE VALUE OF DISCRET...ijsc
In fuzzy decision-making processes based on linguistic information, operations on discrete fuzzy numbers
are commonly performed. Aggregation and defuzzification operations are some of these often used
operations. Many aggregation and defuzzification operators produce results independent to the decisionmaker’s
strategy. On the other hand, the Weighted Average Based on Levels (WABL) approach can take
into account the level weights and the decision maker's "optimism" strategy. This gives flexibility to the
WABL operator and, through machine learning, can be trained in the direction of the decision maker's
strategy, producing more satisfactory results for the decision maker. However, in order to determine the
WABL value, it is necessary to calculate some integrals. In this study, the concept of WABL for discrete
trapezoidal fuzzy numbers is investigated, and analytical formulas have been proven to facilitate the
calculation of WABL value for these fuzzy numbers. Trapezoidal and their special form, triangular fuzzy
numbers, are the most commonly used fuzzy number types in fuzzy modeling, so in this study, such numbers
have been studied. Computational examples explaining the theoretical results have been performed.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Comparison of Bayesian and non-Bayesian estimations for Type-II censored Gen...IqraHussain31
Conference Research Article
Presented By
Iqra Sardar
16th International Conference on Statistical Sciences:
At Department of Statistics
Islamia College, Peshawar Khyber Pakhtunkhwa, Pakistan
Predicting Moscow Real Estate Prices with Azure Machine LearningLeo Salemann
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data.
Predicting Moscow Real Estate Prices with Azure Machine LearningKarunakar Kotha
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data
Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry
In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
Predicting Moscow Real Estate Prices with Azure Machine LearningLeo Salemann
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data.
Predicting Moscow Real Estate Prices with Azure Machine LearningKarunakar Kotha
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data
Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry
In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
Modeling selection pressure in XCS for proportionate and tournament selection
Elements Of Economertics 2nd
1. CONTENTS
PART ONE Basic Statistical Theory
9. Estimation with Deficient Data
1. Introduction to Statistical Inference 9-1 Errors of Measurement 346
1-1 Basic Concepts of Statistical Inference 3 9-2 Estimation from Grouped Data 366
1-2 The Nature of Statistical Inference 7 9-3 Estimation When Some Observations Are Missing 379
1- 3 Sampling Distributions 9 Exercises 388
1 -4 Properties of Sampling Distributions 12 10. Multiple Regression
1 -5 Derivation of Sampling Distributions 15 10-1 Estimation of Regression Parameters 392
Exercises 16 10-2 Further Results of Statistical Inference 403
2. Experimental Derivation of Sampling Distributions 10-3 Multicollinearity 430
10-4 Specification Errors 442
2- 1 Sampling Distribution of Sample Proportion of Exercises 455
Successes 20 11. Formulation and Estimation of Special Models
2- 2 Sampling Distribution of Sample Mean 24 11-1 Models with Binary Regressors 461 +
Exercises 29 11-2 Models with Restricted Coefficients 476
3. Probability and Probability Distributions 11-3 Nonlinear Models 503
3- 1 Sets and Sample Spaces 34 11-4 Distributed Lag Models 527
3-2 Permutations and Combinations 36 11-5 Models with Qualitative Dependent Variables 547
3-3 Basic Theorems of Probability Theory 43 11 -6 Models with Limited Dependent Variables 560
3-4 Bayes Theorem 50 11-7 Models with Varying Coefficients 566
11-8 Models with Unobservable Variables 579
3-5 Discrete Random Variables and Probability 11-9 Disequilibrium Models 587
Functions 53 11-10 Model Choice 593
3-6 Continuous Random Variables and Probability Fun Exercises 600
ctions 59 12. Generalized Linear Regression Model and Its Applications
3- 7 Mathematical Expectation 62 12-1 Generalized Linear Regression Model 607
Exercises 72 12-2 Pooling of Cross-section and Time-Series Data 616
4. Theoretical Derivation of Sampling Distributions 12-3 Seemingly Unrelated Regressions 635
4- 1 Sampling Distribution of Sample Proportion of Successes: Exercises 648
Binomial Distribution 78 13. Simultaneous Equation Systems 651
4-2 Normal Distribution as the Limiting Case of Binomial 13-1 Description of Simultaneous Equation Systems 652
Distribution 85 13-2 The Identification Problem 660
4- 3 Sampling Distribution of Sample Mean 97 13-3 Single-Equation Methods of Estimation 672
Exercises 108 13-4 System Methods of Estimation 695
5. Tests of Hypotheses 13-5 Estimation of Models with Nonspherical Disturbances 704
5- 1 Design and Evaluation of Tests 110 13-6 Comparison of Alternative Methods of Estimation and
5- 2 Distribution of Selected Test Statistics 135 Special Topics 711
Exercises 152 13-7 Analysis of Dynamic Econometric Models 723
6. Estimation Exercises 731
6- 1 Properties of Estimators 156 APPENDIX 735
6-2 Methods of Estimation 172 A. Algebra of Summations 735
6-3 Confidence Intervals 187 B. Elements of Matrix Algebra 738
6-4 Bayesian Inference 192 C. Asymptotic Distributions in Regression Models with Stochastic
Exercises 198 Explanatory Variables by E. P. Howrey and S. H. Hymans 749
PART TWO Basic Econometric Theory D. Statistical Tables 758
7. Simple Regression E. The Greek Alphabet 771
7-1 Relations Between Variables 203 INDEX 773
7-2 The Regression Model 207
7-3 Estimation of the Regression Parameters 211
7- 4 Further Results of Statistical Inference 224
Exercises 256
8. Violations of Basic Assumptions
8- 1 Nonnormality and Nonzero Mean 261
8-?. I leteroskedasticity 269
Autocorrelated Disturbances 298
8-4 Stochastic Explanatory Variable 334
Exercises 341