
Survey Sampling Theory and Applications
Description
Key Features
- Covers a wide spectrum of topics on survey sampling and statistics
- Serves as an ideal text for graduate students and researchers in survey sampling theory and applications
- Contains material on recent developments in survey sampling not covered in other books
- Illustrates theories using numerical examples and exercises
Readership
Undergraduates and graduate students in statistics and mathematics; statisticians in Government and private sector organizations
Table of Contents
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
Product details
- No. of pages: 930
- Language: English
- Copyright: © Academic Press 2017
- Published: March 8, 2017
- Imprint: Academic Press
- eBook ISBN: 9780128118979
- Paperback ISBN: 9780128118481
About the Author
Raghunath Arnab
Affiliations and Expertise
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