Sampling: (Wiley Series in Probability & Mathematical Statistics: Applied Probability & Statistics)

From the reviews of the First Edition: "A well-written book that deserves to be widely used, both by statistics teachers and by researchers." -Biometrics "The explanations are clear and concise, the presentation is extremely pleasing, the references are up to date, and there is an abundance of examples." -Short Book Reviews "This is a highly recommended acquisition for any statistician concerned with the collection of sample information." -Technometrics All aspects of obtaining, interpreting, and using sample data in a new revised volume Sampling provides an up-to-date treatment of both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard-to-detect populations. This Second Edition's extensive new material includes descriptions of new developments, a wider range of approaches to common problems, historical notes, and increased coverage of topics such as methods that combine design and model-based approaches, adjusting for nonsampling errors, and the use of link-tracing designs. Updated chapters show how relevant sampling methods function within such fields as the biological, environmental, and geological sciences; social and health sciences; and sampling the Internet. Organized into six sections, this edition covers basic sampling, from simple random to unequal probability sampling; the use of auxiliary data with ratio estimators and regression estimation; sufficient data, model, and design in practical sampling; useful designs such as stratified, cluster and systematic, multistage, double and network sampling; detectability methods for elusive populations; spatial sampling; and adaptive sampling designs. Additional derivations, notes on underlying ideas, exercises, and examples foster a greater mastery of the presented techniques. New numerical examples, small-population examples, and exercises have been added. Featuring a wider range of topics than other sampling books, Sampling, Second Edition is the ideal reference for scientific researchers and other professionals who use sampling, as well as students in sampling courses.

Table of Contents:
Preface to the Second Edition. Preface to the First Edition. 1. Introduction. 1.1. Basic Ideas of Sampling and Estimation. 1.2. Sampling Units. 1.3. Sampling and Nonsampling Errors. 1.4. Models in Sampling. 1.5. Adaptive and Nonadaptive Designs. 1.6. Some Sampling History. PART I: BASIC SAMPLING. 2. Simple Random Sampling. 2.1. Selecting a Simple Random Sample. 2.2. Estimating the Population Mean. 2.3. Estimating the Population Total. 2.4. Some Underlying Ideas. 2.5. Random Sampling with Replacement. 2.6. Derivations for Random Sampling. 2.7. Model Based Approach to Sampling. 3. Confidence Intervals. 3.1. Confidence Interval for the Population Mean or Total. 3.2. Finite Population Central Limit Theorem. 4. Sample Size. 4.1. Sample Size for Estimating a Population Mean. 4.2. Sample Size for Estimating a Population Total. 4.3. Sample Size for Relative Precision. 5. Estimating Proportions, Ratios, and Subpopulation Means. 5.1. Estimating a Population Proportion. 5.2. Confidence Interval for a Proportion. 5.3. Sample Size for Estimating a Proportion. 5.4. Sample Size for Estimating Several Proportions Simultaneously. 5.5. Estimating a Ratio. 5.6. Estimating a Mean, Total, or Proportion of a Subpopulation. 6. Unequal Probability Sampling. 6.1. Sampling with Replacement: The Hansen Hurwitz Estimator. 6.2. Any Design: The Horvitz Thompson Estimator. 6.3. Generalized Unequal Probability Estimator. 6.4. Small Population Example. 6.5. Derivations and Comments. PART II: MAKING THE BEST USE OF SURVEY DATA. 7. Auxiliary Data and Ratio Estimation. 7.1. Ratio Estimator. 7.2. Small Population Illustrating Bias. 7.3. Derivations and Approximations for the Ratio Estimator. 7.4. Finite Population Central Limit Theorem for the Ratio Estimator. 7.5. Ratio Estimation with Unequal Probability Designs. 7.6. Models in Ratio Estimation. 7.7. Design Implications of Ratio Models. 8. Regression Estimation. 8.1. Linear Regression Estimator. 8.2. Regression Estimation with Unequal Probability Designs. 8.3. Regression Model. 8.4. Multiple Regression Models. 8.5. Design Implications of Regression Models. 9. The Sufficient Statistic in Sampling. 9.1. The Set of Distinct, Labeled Observations. 9.2. Estimation in Random Sampling with Replacement. 9.3. Estimation in Probability Proportional to Size Sampling. 9.4. Comments on the Improved Estimates. 10. Design and Model. 10.1. Uses of Design and Model in Sampling. 10.2. Connections between the Design and Model Approaches. 10.3. Some Comments. 10.4. Likelihood Function in Sampling. PART III SOME USEFUL DESIGNS. 11. Stratified Sampling. 11.1. Estimating the Population Total. 11.2. Estimating the Population Mean. 11.3. Confidence Intervals. 11.4. The Stratification Principle. 11.5. Allocation in Stratified Random Sampling. 11.6. Poststratification. 11.7. Population Model for a Stratified Population. 11.8. Derivations for Stratified Sampling. 12. Cluster and Systematic Sampling. 12.1. Primary Units Selected by Simple Random Sampling. 12.2. Primary Units Selected with Probabilities Proportional to Size. 12.3. The Basic Principle. 12.4. Single Systematic Sample. 12.5. Variance and Cost in Cluster and Systematic Sampling. 13. Multistage Designs. 13.1. Simple Random Sampling at Each Stage. 13.2. Primary Units Selected with Probability Proportional to Size. 13.3. Any Multistage Design with Replacement. 13.4. Cost and Sample Sizes. 13.5. Derivations for Multistage Designs. 14. Double or Two Phase Sampling. 14.1. Ratio Estimation with Double Sampling. 14.2. Allocation in Double Sampling for Ratio Estimation. 14.3. Double Sampling for Stratification. 14.4. Derivations for Double Sampling. 14.5. Nonsampling Errors and Double Sampling. PART IV METHODS FOR ELUSIVE AND HARD TO DETECT POPULATIONS. 15. Network Sampling and Link Tracing Designs. 15.1. Estimation of the Population Total or Mean. 15.2. Derivations and Comments. 15.3. Stratification in Network Sampling. 15.4. Other Link Tracing Designs. 16. Detectability and Sampling. 16.1. Constant Detectability over a Region. 16.2. Estimating Detectability. 16.3. Effect of Estimated Detectability. 16.4. Detectability with Simple Random Sampling. 16.5. Estimated Detectability and Simple Random Sampling. 16.6. Sampling with Replacement. 16.7. Derivations. 16.8. Unequal Probability Sampling of Groups with Unequal Detection Probabilities. 16.9. Derivations. 17. Line and Point Transects. 17.1. Density Estimation Methods for Line Transects. 17.2. Narrow Strip Method. 17.3. Smooth by Eye Method. 17.4. Parametric Methods. 17.5. Nonparametric Methods. 17.6. Designs for Selecting Transects. 17.7. Random Sample of Transects. 17.8. Systematic Selection of Transects. 17.9. Selection with Probability Proportional to Length. 17.10. Note on Estimation of Variance for the Kernel Method. 17.11. Some Underlying Ideas about Line Transects. 17.12. Detectability Imperfect on the Line or Dependent on Size. 17.13. Estimation Using Individual Detectabilities. 17.14. Detectability Functions Other Than Line Transects. 17.15. Variable Circular Plots or Point Transects. 18. Capture Recapture Sampling. 18.1. Single Recapture. 18.2. Models for Simple Capture Recapture. 18.3. Sampling Design in Capture Recapture: Ratio Variance Estimator. 18.4. Estimating Detectability with Capture Recapture Methods. 18.5. Multiple Releases. 18.6. More Elaborate Models. 19. Line Intercept Sampling. 19.1. Random Sample of Lines: Fixed Direction. 19.2. Lines of Random Position and Direction. PART V SPATIAL SAMPLING. 20. Spatial Prediction or Kriging. 20.1. Spatial Covariance Function. 20.2. Linear Prediction (Kriging). 20.3. Variogram. 20.4. Predicting the Value over a Region. 20.5. Derivations and Comments. 21. Spatial Designs. 21.1. Design for Local Prediction. 21.2. Design for Prediction of Mean of Region. 22. Plot Shapes and Observational Methods. 22.1. Observations from Plots. 22.2. Observations from Detectability Units. 22.3. Comparisons of Plot Shapes and Detectability Methods. PART VI ADAPTIVE SAMPLING. 23. Adaptive Sampling Designs. 23.1. Adaptive and Conventional Designs and Estimators. 23.2. Brief Survey of Adaptive Sampling. 24. Adaptive Cluster Sampling. 24.1. Designs. 24.2. Estimators. 24.3. When Adaptive Cluster Sampling Is Better Than Simple Random Sampling. 24.4. Expected Sample Size, Cost, and Yield. 24.5. Comparative Efficiencies of Adaptive and Conventional Sampling. 24.6. Further Improvement of Estimators. 24.7. Derivations. 24.8. Data for Examples and Figures. 25. Systematic and Strip Adaptive Cluster Sampling. 25.1. Designs. 25.2. Estimators. 25.3. Calculations for Adaptive Cluster Sampling Strategies. 25.4. Comparisons with Conventional Systematic and Cluster Sampling. 25.5. Derivations. 25.6. Example Data. 26. Stratified Adaptive Cluster Sampling. 26.1. Designs. 26.2. Estimators. 26.3. Comparisons with Conventional Stratified Sampling. 26.4. Further Improvement of Estimators. 26.5. Example Data. Answers to Selected Exercises. References. Author Index. Subject Index.

About the Author :
STEVEN K. THOMPSON, PhD, is Professor of Statistics at the Pennsylvania State University. During his career he has served on the faculties of the University of Auckland and the University of Alaska. He is the author of Adaptive Sampling (with George Seber), also published by Wiley.

Review :
"This is an informative and clearly written book that will serve as a useful reference for researchers and as an introduction for students " (Statistics in Medical Research, October 2005) "...treats both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard to detect populations." (SciTech Book News, Vol. 26, No. 2, June 2002) "...a well written and expanded new edition...an excellent book on an important subject. Highly recommended..." (Choice, Vol. 40, No. 2, October 2002) "This book has never had a competitor. It is the only book that takes a broad approach to sampling...any good personal statistics library should include a copy of this book." (Technometrics, Vol. 44, No. 4, November 2002) "...an ideal reference for scientific researchers and other professionals who use sampling..."(Zentralblatt Math, Vol.1002, No.02, 2003)