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Survey Sampling: Theory and Applications

Raghunath Arnab
Publisher: 
Academic Press
Publication Date: 
2017
Number of Pages: 
930
Format: 
Paperback
Price: 
185.00
ISBN: 
9780128118481
Category: 
Textbook
We do not plan to review this book.

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