Sampling Statistics
Published on 17. August 2009
480 pages
978-0-470-52354-4 (ISBN)
Description
Discover the latest developments and current practices in surveysampling
Survey sampling is an important component of research in manyfields, and as the importance of survey sampling continues to grow,sophisticated sampling techniques that are both economical andscientifically reliable are essential to planning statisticalresearch and the design of experiments. Sampling Statisticspresents estimation techniques and sampling concepts to facilitatethe application of model-based procedures to survey samples.
The book begins with an introduction to standard probabilitysampling concepts, which provides the foundation for studyingsamples selected from a finite population. The development of thetheory of complex sampling methods is detailed, and subsequentchapters explore the construction of estimators, sample design,replication variance estimation, and procedures such as nonresponseadjustment and small area estimation where models play a key role.A final chapter covers analytic studies in which survey data areused for the estimation of parameters for a subject mattermodel.
The author draws upon his extensive experience with surveysamples in the book's numerous examples. Both the production of"general use" databases and the analytic study of a limited numberof characteristics are discussed. Exercises at the end of eachchapter allow readers to test their comprehension of the presentedconcepts and techniques, and the references provide furtherresources for study.
Sampling Statistics is an ideal book for courses insurvey sampling at the graduate level. It is also a valuablereference for practicing statisticians who analyze survey data orare involved in the design of sample surveys.
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WAYNE A. FULLER, PhD, is Distinguished Professor Emeritus in the Departments of Statistics and Economics at Iowa State University. He has previously served as principal investigator on research agreements with the U.S. Census Bureau, the Natural Resources Conservation Service, the National Agricultural Statistics Service, and the Bureau of Labor Statistics. Dr. Fuller is also a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Biometric Society, and is an elected member of the International Statistical Institute. He is the author of Introduction to Statistical Time Series and Measurement Error Models, both published by Wiley.
Content
Preface.
List Of Tables.
List of Principal Results.
List Of Examples.
1 PROBABILITY SAMPLING FROM A FINITE UNIVERSE.
1.1 Introduction.
1.2 Probability Sampling.
1.3 Limit Properties.
1.4 Methods of Unequal Probability Sample Selection.
1.5 References.
1.6 Exercises.
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.
2.7 Calibration.
2.8 Weight Bounds.
2.9 Maximum Likelihood and Raking Ratio.
2.10 References.
2.11 Exercises.
2.12 Appendix 2A: Missouri Data.
3 USE OF AUXILIARY INFORMATION IN DESIGN.
3.1 Introduction.
3.2 Multiple-Stage Samples.
3.3 Multiple-Phase Samples.
3.4 Rejective sampling.
3.5 References.
3.6 Exercises.
4 REPLICATION VARIANCE ESTIMATION.
4.1 Introduction.
4.2 Jackknife Variance Estimation.
4.3 Balanced Half-Samples.
4.4 Two-Phase Samples.
4.5 The Bootstrap.
4.6 References.
4.7 Exercises.
5 MODELS USED IN CONJUNCTION WITH SAMPLING.
5.1 Nonresponse.
5.2 Imputation.
5.3 Variance Estimation.
5.4 Outliers and Skewed Populations.
5.5 Small Area Estimation.
5.6 Measurement Error.
5.7 References.
5.8 Exercises.
6 ANALYTIC STUDIES.
6.1 Introduction.
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.
6.8 References.
6.9 Exercises.
REFERENCES.
Index.
