Fuller W.A. Sampling Statistics

John Wiley & Sons, Inc., 2009. — 472 p. — ISBN: 0470454601, 9780470454602.Discover the latest developments and current practices in survey sampling 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.Probability Sampling from a Finite UniverseProbability Sampling
Limit Properties
Methods of Unequal Probability Sample SelectionExercises
Appendix 1: Some Order Concepts
Use of Auxilary Information in Estimation
Ratio Estimation
Regression Estimation
Models and Regression Estimation
Regression and Stratification
Estimation with Conditional Probabilities
Regression for Two-Stage Samples
Calibration
Weight Bounds
Maximum Likelihood and Raking RatioExercises
Appendix 2: Missouri Data
Use of Auxilary Information in DesignMultiple-Stage Samples
Multiple-Phase Samples
Rejective samplingExercises
Replication Variance EstimationJackknife Variance Estimation
Balanced Half-Samples
Two-Phase Samples
The BootstrapExercises
Models Used in Conjuction with Sampling
Nonresponse
Imputation
Variance Estimation
Outliers and Skewed Populations
Small Area Estimation
Measurement ErrorAnalytic StudiesModels and Simple Estimators
Estimation of Regression Coefficients
Instrumental variables
Nonlinear models
Cluster and multistage samples
Pretest proceduresExercises