Suitable for graduate students and researchers in many fields, this book has become the classic reference for a model-assisted approach to sampling theory. It provides a comprehensive account of survey sampling theory and methodology. A central theme is to show how statistical modeling is a vital component of the sampling process and in the choice of estimation technique. The book covers classical topics as well as areas where significant new developments have taken place notably domain estimation, variance estimation, methods for handling nonresponse, models for measurement error, and the analysis of survey data.

Zusammenfassung
Now available in paperback, this book provides a comprehensive account of survey sampling theory and methodology suitable for students and researchers across a variety of disciplines. The first textbook that systematically extends traditional sampling theory with the aid of a modern model assisted outlook.

Inhalt
I Principles of Estimation for Finite Populations and Important Sampling Designs.- 1 Survey Sampling in Theory and Practice.- 2 Basic Ideas in Estimation from Probability Samples.- 3 Unbiased Estimation for Element Sampling Designs.- 4 Unbiased Estimation for Cluster Sampling and Sampling in Two or More Stages.- 5 Introduction to More Complex Estimation Problems.- II Estimation through Linear Modeling, Using Auxiliary Variables.- 6 The Regression Estimator.- 7 Regression Estimators for Element Sampling Designs.- 8 Regression Estimators for Cluster Sampling and Two-Stage Sampling.- III Further Questions in Design and Analysis of Surveys.- 9 Two-Phase Sampling.- 10 Estimation for Domains.- 11 Variance Estimation.- 12 Searching for Optimal Sampling Designs.- 13 Further Statistical Techniques for Survey Data.- IV A Broader View of Errors in Surveys.- 14 Nonsampling Errors and Extensions of Probability Sampling Theory.- 15 Nonresponse.- 16 Measurement Errors.- 17 Quality Declarations for Survey Data.- Appendix A Principles of Notation.- Appendix B The MU284 Population.- Appendix C The Clustered MU284 Population.- Appendix D The CO124 Population.- References.- Answers to Selected Exercises.- Author Index.