Survey Sampling

by Parimal Mukhopadhyay

Survey Sampling reviews aspects of statistical inference in finite population sampling. After reviewing important results in fixed population models, the book considers in detail the model-dependent optimal strategies of Royall and Herson (1973) and the robustification of these strategies under model breakdown. It then reviews model assist strategies of Cassel, Sarndal and Wretman (1976) and Wright (1983). Asymptotic properties of these strategies are also reviewed.

Another approach is design-based calibration of Deville and Sarndal (1992). Design-based conditional inference is an alternative approach which has drawn considerable attention to survey statisticians. Lastly the book reviews two important tools of making inference,-Model Calibration approach and Empirical Likelihood based approach. Post-stratification is another aspect which requires considerable attention.

Table of Contents

• Preface
• The Preliminaries
• Some Inferential Problems under Fixed Population Set-up
• Model-Dependent Optimal Strategies
• Robustness of Model-Dependent Optimal Strategies
• Model-Assisted Sampling Strategies
• Asymptotically Optimum Sampling Strategies
• Robust Strategies
• Design-Based Conditional Approach
• The Design-Based Calibration Approach
• Model Based Calibration Approach
• Empirical Likelihood Approach
• Referenced
• Author Index
• Subject Index.

268 pages, Hardcover

First published January 1, 2007