دانلود کتاب A Comprehensive Textbook on Sample Surveys (Indian Statistical Institute Series)

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Preface
References
About This Book
Introduction
References
Contents
About the Authors
1 Meaning and Purpose of Survey Sampling
1.1 Definitions
1.2 Random Sampling
1.3 Estimation Preliminaries
1.4 Requirements of a Good Sample
1.5 Lessons and Exercises
References
2 Inference in Survey Sampling
2.1 Traditional or Design-Based Approach: Its Uses and Limitations
2.2 Sampling Designs and Strategies
2.3 Inclusion Probabilities and Their Inter-Relationships
2.4 Necessary and Sufficient Conditions for Existence of Unbiased Estimators
2.5 Linear and Homogeneous Linear Unbiased Estimators
2.6 Godambe\'s and Basu\'s Theorems on Non-Existence of Unbiased Estimators and Exceptional Situations
2.7 Sufficiency and Minimal Sufficiency
2.8 Complete Class Theorem
2.9 Comparison Between SRSWR and SRSWOR
2.10 Roles of Labels and of Distinct Units in `With Replacement\' Sample
2.11 Controlled Sampling
2.12 Minimax Approach
2.13 Lessons and Exercises
References
3 Sampling with Varying Probabilities
3.1 Stratified Sampling
3.2 Allocation of Sample-Size
3.3 Construction of Strata
3.4 Post-Stratification
3.5 Sample Selection with Probability Proportional to Size with Replacement and Its Various Amendments
3.5.1 `Probability proportional to size\' or PPS Method of Sample Selection is an Attempt Towards Enhancing Efficiency in Estimation
3.5.2 Probability Proportional to Size without Replacement Sampling (PPSWOR) and Estimation
3.5.3 Murthy\'s Sampling Strategy
3.5.4 A Special Case of Murthy\'s Strategy: Lahiri\'s (ch3Lahiri51) Ratio, Estimation From his Sample Selection Method
3.5.5 General Probability Sampling Schemes and Horvitz–Thompson Estimator
3.5.6 ch3Rao62 Strategy
3.6 Systematic Sampling
3.7 Modified Systematic Sampling
3.8 Cluster Sampling
3.9 Multi-stage Sampling
3.10 Ratio and Regression Methods of Estimation
3.11 Almost Unbiased Ratio Estimators
3.12 Multi-phase Sampling
3.12.1 Double Sampling in Stratification
3.12.2 Double Sampling Ratio Estimation
3.12.3 Double Sampling for Regression Estimator
3.12.4 Double Sampling in Tackling Non-response
3.13 Sampling on Successive Occasions
3.14 Panel Rotation Sampling
3.15 Lessons
References
4 Fixing the Size of an Equal Probability Sample
4.1 Sample Size Determination in SRSWR
4.2 Controlled Sampling
4.3 Lessons and Exercises
References
5 Adjusting Unit-Nonresponse by Weighting and Tackling Item-Nonresponse by Imputation
5.1 Weight Adjusting Methods of Unit-Nonresponse
5.1.1 Method of ``Call backs\'\'
5.1.2 Disposing of the `Not-at-Homes\'
5.1.3 A Model-Based Weighting Approach to Counter Unit Non-response
5.2 Imputation Techniques to Control Item Non-responses
5.2.1 Logical, Consistency or Deductive Imputation
5.2.2 Cold Deck Imputation
5.2.3 Mean Value Imputation
5.2.4 Hot Deck Imputation
5.2.5 Random Imputation
5.2.6 Regression Imputation Technique
5.2.7 Multiple Imputation Methods
5.2.8 Repeated Replication Method Imputation
5.3 Lessons
References
6 Randomized Response and Indirect Survey Techniques
6.1 ch6Warner1965: His Innovation
6.2 More RR Devices to Estimate Population Proportion
6.3 When the Stigmatizing Feature is Quantitative
6.4 Lessons, Exercises and Case Study
References
7 Super-Population Modeling. Model-Assisted Approach. Asymptotics
7.1 Super-Population: Concept and Relevance
7.2 Godambe-Thompson\'s (1977) Optimality Approach
7.3 Brewer\'s (ch7Brewer79) Asymptotic Approach
7.4 Further Analysis Under Model-Assisted Approach
7.5 Lessons
References
8 Prediction Approach: Robustness, Bayesian Methods, Empirical Bayes
8.1 Introduction
8.2 Developing Brewer-Royall\'s Prediction Approach in Survey Sampling
8.3 Robustness Issue (A Short Note)
8.4 Bayesian Approach
8.5 Empirical Bayes Approach
8.6 Lessons
References
9 Small Area Estimation and Developing Small Domain Statistics
9.1 Introduction and Formulation of the Problem
9.2 Synthetic Approach for Improved Efficiency
9.3 An Empirical Approach for a Further Potential Efficacy
9.4 Strengthening Small Domain Statistics by Borrowing Data Across Time by Kalman Filtering
9.5 Discussion
9.6 Lessons
References
10 Estimation of Non-linear Parametric Functions
10.1 Linearization
10.2 Jackknife
10.3 The Technique of Replicated and Interpenetrating Network of Subsampling (IPNS)
10.4 Balanced Repeated Replication
10.5 Bootstrap
10.5.1 Naive Bootstrap
10.5.2 ch10RW1988 Rescaling Bootstrap
10.5.3 Modifications of ch10RW1988 Rescaling Bootstrap
10.6 Exercises
References
11 Permanent Random Numbers, Poisson Sampling and Collocated Sampling
11.1 The Permanent Random Number (PRN) Technique
11.2 PRN Sampling Technique for Poisson Sampling
11.3 Sequential Poisson Sampling
11.4 Sequentially Deleting Out-of-Scope Units
11.5 Collocated Sampling
11.6 Exercise
References
12 Network and Adaptive Sampling
12.1 Network Sampling: Procedure and Use
12.2 Adaptive Sampling
12.2.1 Adaptive Sampling: Technique and Use
12.3 Constrained Network Sampling
12.4 Constrained Adaptive Sampling
12.5 A Brief Review of Literature
12.6 Lessons and Exercises
References
13 Fixing Size of a Sample in Complex Strategies
13.1 Unequal Probability Sampling and Unbiased Total-Estimator with Well-Known Variance Formula Available
13.2 Fixing Sample-Size When an Unbiased Variance Estimator is Used
13.3 Fixing Sample-Size When Randomized Response (RR) Survey is to be Designed
13.4 Lessons and Exercises
References
14 Inadequate and Multiple Frame Data and Conditional Inference
14.1 Inadequacy Illustrated of Frames and Way Out Procedures Explained
14.2 Multiple Frame Survey Data
14.3 Conditional Inference
14.4 Lessons and Exercises
References
15 Study of Analytical Surveys
15.1 Goodness of Fit Test
15.2 Test of Independence
15.3 Test of Homogeneity
15.4 Regression Analysis
References
Appendix An Epilogue
References