Survey sampling is an important component of research in many fields, and as the importance of survey sampling continues to grow, sophisticated sampling techniques that are both economical and scientifically reliable are essential to planning statistical research and the design of experiments. Sampling Statistics presents estimation techniques and sampling concepts to facilitate the application of model–based procedures to survey samples.
The book begins with an introduction to standard probability sampling concepts, which provides the foundation for studying samples selected from a finite population. The development of the theory of complex sampling methods is detailed, and subsequent chapters explore the construction of estimators, sample design, replication variance estimation, and procedures such as nonresponse adjustment and small area estimation where models play a key role. A final chapter covers analytic studies in which survey data are used for the estimation of parameters for a subject matter model.
The author draws upon his extensive experience with survey samples in the book′s numerous examples. Both the production of "general use" databases and the analytic study of a limited number of characteristics are discussed. Exercises at the end of each chapter allow readers to test their comprehension of the presented concepts and techniques, and the references provide further resources for study.
Sampling Statistics is an ideal book for courses in survey sampling at the graduate level. It is also a valuable reference for practicing statisticians who analyze survey data or are involved in the design of sample surveys.
List Of Tables.
List of Principal Results.
List Of Examples.
1 PROBABILITY SAMPLING FROM A FINITE UNIVERSE.
1.2 Probability Sampling.
1.3 Limit Properties.
1.4 Methods of Unequal Probability Sample Selection.
1.7 Appendix 1A: Some Order Concepts.
2 USE OF AUXILIARY INFORMATION IN ESTIMATION.
2.1 Ratio Estimation.
2.2 Regression Estimation.
2.3 Models and Regression Estimation.
2.4 Regression and Stratification.
2.5 Estimation with Conditional Probabilities.
2.6 Regression for Two–Stage Samples.
2.8 Weight Bounds.
2.9 Maximum Likelihood and Raking Ratio.
2.12 Appendix 2A: Missouri Data.
3 USE OF AUXILIARY INFORMATION IN DESIGN.
3.2 Multiple–Stage Samples.
3.3 Multiple–Phase Samples.
3.4 Rejective sampling.
4 REPLICATION VARIANCE ESTIMATION.
4.2 Jackknife Variance Estimation.
4.3 Balanced Half–Samples.
4.4 Two–Phase Samples.
4.5 The Bootstrap.
5 MODELS USED IN CONJUNCTION WITH SAMPLING.
5.3 Variance Estimation.
5.4 Outliers and Skewed Populations.
5.5 Small Area Estimation.
5.6 Measurement Error.
6 ANALYTIC STUDIES.
6.2 Models and Simple Estimators.
6.3 Estimation of Regression Coefficients.
6.4 Instrumental variables.
6.5 Nonlinear models.
6.6 Cluster and multistage samples.
6.7 Pretest procedures.