Survey sampling: theory and applications/ Arnab Raghunath

Includes bibliography and index

Chapter 1. Preliminaries and Basics of Probability Sampling
1.1. Introduction
1.2. Definitions and Terminologies
1.3. Sampling Design and Inclusion Probabilities
1.4. Methods of Selection of Sample
1.5. Hanurav's Algorithm
1.6. Ordered and Unordered Sample
1.7. Data
1.8. Sampling From Hypothetical Populations
1.9. Exercises
Chapter 2. Unified Sampling Theory: Design-Based Inference
2.1. Introduction
2.2. Definitions and Terminologies
2.3. Linear Unbiased Estimators
2.4. Properties of the Horvitz–Thompson Estimator
2.5. Nonexistence Theorems
2.6. Admissible Estimators
2.7. Sufficiency in Finite Population
2.8. Sampling Strategies
2.9. Discussions
2.10. Exercises
Chapter 3. Simple Random Sampling
3.1. Introduction
3.2. Simple Random Sampling Without Replacement
3.3. Simple Random Sampling With Replacement
3.4. Interval Estimation
3.5. Determination of Sample Size
3.6. Inverse Sampling
3.7. Exercises
Chapter 4. Systematic Sampling
4.1. Introduction
4.2. Linear Systematic Sampling
4.3. Efficiency of Systematic Sampling
4.4. Linear Systematic Sampling Using Fractional Interval
4.5. Circular Systematic Sampling
4.6. Variance Estimation
4.7. Two-Dimensional Systematic Sampling
4.8. Exercises
Chapter 5. Unequal Probability Sampling
5.1. Introduction
5.2. Probability Proportional to Size With Replacement Sampling Scheme
5.3. Probability Proportional to Size Without Replacement Sampling Scheme
5.4. Inclusion Probability Proportional to Measure of Size Sampling Scheme
5.5. Probability Proportional to Aggregate Size Without Replacement
5.6. Rao–Hartley–Cochran Sampling Scheme
5.7. Comparison of Unequal (Varying) Probability Sampling Designs
5.8. Exercises
Chapter 6. Inference Under Superpopulation Model
6.1. Introduction
6.2. Definitions
6.3. Model-Assisted Inference
6.4. Model-Based Inference
6.5. Robustness of Designs and Predictors
6.6. Bayesian Inference
6.7. Comparison of Strategies Under Superpopulation Models
6.8. Discussions
6.9. Exercises
Chapter 7. Stratified Sampling
7.1. Introduction
7.2. Definition of Stratified Sampling
7.3. Advantages of Stratified Sampling
7.4. Estimation Procedure
7.5. Allocation of Sample Size
7.6. Comparison Between Stratified and Unstratified Sampling
7.7. Construction of Strata
7.8. Estimation of Gain Due To Stratification
7.9. Poststratification
7.10. Exercises
Chapter 8. Ratio Method of Estimation
8.1. Introduction
8.2. Ratio Estimator for Population Ratio
8.3. Ratio Estimator for Population Total
8.4. Biases and Mean-Square Errors for Specific Sampling Designs
8.5. Interval Estimation
8.6. Unbiased Ratio, Almost Unbiased Ratio, and Unbiased Ratio–Type Estimators
8.7. Ratio Estimator for Stratified Sampling
8.8. Ratio Estimator for Several Auxiliary Variables
8.9. Exercises
Chapter 9. Regression, Product, and Calibrated Methods of Estimation
9.1. Introduction
9.2. Difference Estimator
9.3. Regression Estimator
9.4. Product Method of Estimation
9.5. Comparison Between the Ratio, Regression, Product, and Conventional Estimators
9.6. Dual to Ratio Estimator
9.7. Calibration Estimators
9.8. Exercises
Appendix 9A
Chapter 10. Two-Phase Sampling
10.1. Introduction
10.2. Two-Phase Sampling for Estimation
10.3. Two-Phase Sampling for Stratification
10.4. Two-Phase Sampling for Selection of Sample
10.5. Two-Phase Sampling for Stratification and Selection of Sample
10.6. Exercises
Chapter 11. Repetitive Sampling
11.1. Introduction
11.2. Estimation of Mean for the Most Recent Occasion
11.3. Estimation of Change Over Two Occasions
11.4. Estimation of Mean of Means
11.5. Exercises
Chapter 12. Cluster Sampling
12.1. Introduction
12.2. Estimation of Population Total and Variance
12.3. Efficiency of Cluster Sampling
12.4. Probability Proportional to Size With Replacement Sampling
12.5. Estimation of Mean per Unit
12.6. Exercises
Chapter 13. Multistage Sampling
13.1. Introduction
13.2. Two-Stage Sampling Scheme
13.3. Estimation of the Population Total and Variance
13.4. First-Stage Units Are Selected by PPSWR Sampling Scheme
13.5. Modification of Variance Estimators
13.6. More than Two-Stage Sampling
13.7. Estimation of Mean per Unit
13.8. Optimum Allocation
13.9. Self -weighting Design
13.10. Exercises
Chapter 14. Variance/Mean Square Estimation
14.1. Introduction
14.2. Linear Unbiased Estimators
14.3. Nonnegative Variance/Mean Square Estimation
14.4. Exercises
Chapter 15. Nonsampling Errors
15.1. Introduction
15.2. Sources of Nonsampling Errors
15.3. Controlling of Nonsampling Errors
15.4. Treatment of Nonresponse Error
15.5. Measurement Error
15.6. Exercises
Chapter 16. Randomized Response Techniques
16.1. Introduction
16.2. Randomized Response Techniques for Qualitative Characteristics
16.3. Extension to More than One Categories
16.4. Randomized Response Techniques for Quantitative Characteristics
16.5. General Method of Estimation
16.6. Optional Randomized Response Techniques
16.7. Measure of Protection of Privacy
16.8. Optimality Under Superpopulation Model
16.9. Exercises
Chapter 17. Domain and Small Area Estimation
17.1. Introduction
17.2. Domain Estimation
17.3. Small Area Estimation
17.4. Exercises
Chapter 18. Variance Estimation: Complex Survey Designs
18.1. Introduction
18.2. Linearization Method
18.3. Random Group Method
18.4. Jackknife Method
18.5. Balanced Repeated Replication Method
18.6. Bootstrap Method
18.7. Generalized Variance Functions
18.8. Comparison Between the Variance Estimators
18.9. Exercises
Chapter 19. Complex Surveys: Categorical Data Analysis
19.1. Introduction
19.2. Pearsonian Chi-Square Test for Goodness of Fit
19.3. Goodness of Fit for a General Sampling Design
19.4. Test of Independence
19.5. Tests of Homogeneity
19.6. Chi-Square Test Based on Superpopulation Model
19.7. Concluding Remarks
19.8. Exercises
Chapter 20. Complex Survey Design: Regression Analysis
20.1. Introduction
20.2. Design-Based Approach
20.3. Model-Based Approach
20.4. Concluding Remarks
20.5. Exercises
Chapter 21. Ranked Set Sampling
21.1. Introduction
21.2. Ranked Set Sampling by Simple Random Sampling With Replacement Method
21.3. Simple Random Sampling Without Replacement
21.4. Size-Biased Probability of Selection
21.5. Concluding Remarks
21.6. Exercises
Chapter 22. Estimating Functions
22.1. Introduction
22.2. Estimating Function and Estimating Equations
22.3. Estimating Function From Superpopulation Model
22.4. Estimating Function for a Survey Population
22.5. Interval Estimation
22.6. Nonresponse
22.7. Concluding Remarks
22.8. Exercises
Chapter 23. Estimation of Distribution Functions and Quantiles
23.1. Introduction
23.2. Estimation of Distribution Functions
23.3. Estimation of Quantiles
23.4. Estimation of Median
23.5. Confidence Interval for Distribution Function and Quantiles
23.6. Concluding Remarks
23.7. Exercises
Chapter 24. Controlled Sampling
24.1. Introduction
24.2. Pioneering Method
24.3. Experimental Design Configurations
24.4. Application of Linear Programming
24.5. Nearest Proportional to Size Design
24.6. Application of Nonlinear Programming
24.7. Coordination of Samples Overtime
24.8. Discussions
24.9. Exercises
Chapter 25. Empirical Likelihood Method in Survey Sampling
25.1. Introduction
25.2. Scale Load Approach
25.3. Empirical Likelihood Approach
25.4. Empirical Likelihood for Simple Random Sampling
25.5. Pseudo–empirical Likelihood Method
25.6. Asymptotic Behavior of MPEL Estimator
25.7. Empirical Likelihood for Stratified Sampling
25.8. Model-Calibrated Pseudoempirical Likelihood
25.9. Pseudo–empirical Likelihood to Raking
25.10. Empirical Likelihood Ratio Confidence Intervals
25.11. Concluding Remarks
25.12. Exercises
Chapter 26. Sampling Rare and Mobile Populations
26.1. Introduction
26.2. Screening
26.3. Disproportionate Sampling
26.4. Multiplicity or Network Sampling
26.5. Multiframe Sampling
26.6. Snowball Sampling
26.7. Location Sampling
26.8. Sequential Sampling
26.9. Adaptive Sampling
26.10. Capture–Recapture Method
26.11. Exercises

Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends. The text is useful for basic and advanced survey sampling courses. Many other books available for graduate students do not contain material on recent developments in the area of survey sampling.

The book covers a wide spectrum of topics on the subject, including repetitive sampling over two occasions with varying probabilities, ranked set sampling, Fays method for balanced repeated replications, mirror-match bootstrap, and controlled sampling procedures. Many topics discussed here are not available in other text books. In each section, theories are illustrated with numerical examples. At the end of each chapter theoretical as well as numerical exercises are given which can help graduate students.