Survey Sampling Theory and Applications

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