Sampling /

Sampling /

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Bibliographic Details
Author / Creator:Thompson, Steven K., 1945-
Edition:3rd ed.
Imprint:Hoboken, N.J. : Wiley, c2012.
Description:xxi, 436 p. : ill. ; 25 cm.
Language:English
Series:Wiley series in probability and statistics
Wiley series in probability and statistics.
Subject:
Sampling (Statistics)
Sampling (Statistics)
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8737713
Hidden Bibliographic Details
ISBN:9780470402313 (hardback)
0470402318 (hardback)
Notes:Machine generated contents note: Preface to the Third EditionPreface to the Second Edition xiiiPreface to the First Edition xv1. Introduction 1Part I. Basic Sampling 92. Simple Random Sampling 113. Confidence Intervals 294. Sample Size 355. Estimating Proportions, Ratios, and Subpopulation Means 396. Unequal Probability Sampling 51Part II. Making the Best Use of Survey Data 657. Auxiliary Data and Ratio Estimation 678. Regression Estimation 899. The Sufficient Statistic in Sampling 10110. Design and Model 107Part III. Some Useful Designs 11511. Stratified Sampling 11712. Cluster and Systematic Sampling 12913. Multistage Designs 14314. Double or Two-Phase Sampling 157Part IV. Methods for Elusive and Hard-to-Detect Populations 17115. Network Sampling and Link-Tracing Designs 17316. Detectability and Sampling 18517. Line and Point Transects 19918. Capture-Recapture Sampling 23319. Line-Intercept Sampling 245Part V. Spatial Sampling 25520. Spatial Prediction or Kriging 25721. Spatial Designs 27122. Plot Shapes and Observational Methods 275Part VI. Adaptive Sampling 28323. Adaptive Sampling Designs 28524. Adaptive Cluster Sampling 28925. Systematic and Strip Adaptive Cluster Sampling 30926. Stratified Adaptive Cluster Sampling 323Answers to Selected Exercises 339References 343Author Index 361Subject Index 365 .
Includes bibliographical references and indexes.
Summary:"The Third Edition retains the general organization of the prior two editions, but it incorporates new material throughout the text. The book is organized into six parts: Part I covers basic sampling from simple random sampling to unequal probability sampling; Part II treats the use of auxiliary data with ratio and regression estimation and looks at the ideas of sufficient data, model, and design in practical sampling; Part III covers major useful designs such as stratified, cluster and systematic, multistage, and double and network sampling; Part IV examines detectability methods for elusive populations, and basic problems in detectability, visibility, and catchability are discussed; Part V concerns spatial sampling with the prediction methods of geostatistics, considerations of efficient spatial designs, and comparisons of different observational methods including plot shapes and detection aspects; and Part VI introduces adaptive sampling designs in which the sampling procedure depends on what is observed during the survey. For this new edition, the author has focused on thoroughly updating the book with a special emphasis on the first 14 chapters since these topics are invariably covered in basic sampling courses. The author has also implemented new approaches to explain the various techniques in the book, and as a result, new examples and explanations have been added throughout. In an effort to improve the presentation and visualization of the book, new figures as well as replacement figures for previously existing figures have been added. This book has continuously stood out from other sampling texts since the figures evoke the idea of each sampling design. The new figures will help readers to better visualize and understand the underlying concepts such as the different sampling strategies"--
Table of Contents:
  • Preface
  • 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
  • 2.8. Computing Notes
  • Entering Data in R
  • Sample Estimates
  • Simulation
  • Further Comments on the Use of Simulation
  • Exercises
  • 3. Confidence Intervals
  • 3.1. Confidence Interval for the Population Mean or Total
  • 3.2. Finite-Population Central Limit Theorem
  • 3.3. Sampling Distributions
  • 3.4. Computing Notes
  • Confidence Interval Computation
  • Simulations Illustrating the Approximate Normality of a Sampling Distribution with Small n and N
  • Daily Precipitation Data
  • Exercises
  • 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
  • Exercises
  • 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
  • Estimating a Subpopulation Mean
  • Estimating a Proportion for a Subpopulation
  • Estimating a Subpopulation Total
  • Exercises
  • 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
  • 6.6. Computing Notes
  • Writing an R Function to Simulate a Sampling Strategy
  • Comparing Sampling Strategies
  • Exercises
  • 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
  • Types of Estimators for a Ratio
  • 7.7. Design Implications of Ratio Models
  • 7.8. Computing Notes
  • Exercises
  • 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
  • Exercises
  • 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
  • With Any Stratified Design
  • With Stratified Random Sampling
  • 11.2. Estimating the Population Mean
  • With Any Stratified Design
  • With Stratified Random Sampling
  • 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
  • Optimum Allocation
  • Poststratification Variance
  • 11.9. Computing Notes
  • Exercises
  • 12. Cluster and Systematic Sampling
  • 12.1. Primary Units Selected by Simple Random Sampling
  • Unbiased Estimator
  • Ratio Estimator
  • 12.2. Primary Units Selected with Probabilities Proportional to Size
  • Hansen-Hurwitz (PPS) Estimator
  • Horvitz-Thompson Estimator
  • 12.3. The Basic Principle
  • 12.4. Single Systematic Sample
  • 12.5. Variance and Cost in Cluster and Systematic Sampling
  • 12.6. Computing Notes
  • Exercises
  • 13. Multistage Designs
  • 13.1. Simple Random Sampling at Each Stage
  • Unbiased Estimator
  • Ratio Estimator
  • 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
  • Unbiased Estimator
  • Ratio Estimator
  • Probability-Proportional-to-Size Sampling
  • More Than Two Stages
  • Exercises
  • 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
  • Approximate Mean and Variance: Ratio Estimation
  • Optimum Allocation for Ratio Estimation
  • Expected Value and Variance: Stratification
  • 14.5. Nonsampling Errors and Double Sampling
  • Nonresponse, Selection Bias, or Volunteer Bias
  • Double Sampling to Adjust for Nonresponse: Callbacks
  • Response Modeling and Nonresponse Adjustments
  • 14.6. Computing Notes
  • Exercises
  • 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
  • Multiplicity Estimator
  • Horvitz-Thompson Estimator
  • 15.2. Derivations and Comments
  • 15.3. Stratification in Network Sampling
  • 15.4. Other Link-Tracing Designs
  • 15.5. Computing Notes
  • Exercises
  • 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
  • Exercises
  • 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
  • Estimating f (0) by the Kernel Method
  • Fourier Series Method
  • 17.6. Designs for Selecting Transects
  • 17.7. Random Sample of Transects
  • Unbiased Estimator
  • Ratio Estimator
  • 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
  • Line Transects and Detectability Functions
  • Single Transect
  • Average Detectability
  • Random Transect
  • Average Detectability and Effective Area
  • Effect of Estimating Detectability
  • Probability Density Function of an Observed Distance
  • 17.12. Detectability Imperfect on the Line or Dependent on Size
  • 17.13. Estimation Using Individual Detectabilities
  • Estimation of Individual Detectabilities
  • 17.14. Detectability Functions other than Line Transects
  • 17.15. Variable Circular Plots or Point Transects
  • Exercise
  • 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
  • Random Sampling with Replacement of Detectability Units
  • Random Sampling without Replacement
  • 18.4. Estimating Detectability with Capture-Recapture Methods
  • 18.5. Multiple Releases
  • 18.6. More Elaborate Models
  • Exercise
  • 19. Line-Intercept Sampling
  • 19.1. Random Sample of Lines: Fixed Direction
  • 19.2. Lines of Random Position and Direction
  • Exercises
  • 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
  • 20.6. Computing Notes
  • Exercise
  • 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
  • Initial Simple Random Sample without Replacement
  • Initial Random Sample with Replacement
  • 24.2. Estimators
  • Initial Sample Mean
  • Estimation Using Draw-by-Draw Intersections
  • Estimation Using Initial Intersection Probabilities
  • 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
  • Exercises
  • 25. Systematic and Strip Adaptive Cluster Sampling
  • 25.1. Designs
  • 25.2. Estimators
  • Initial Sample Mean
  • Estimator Based on Partial Selection Probabilities
  • Estimator Based on Partial Inclusion Probabilities
  • 25.3. Calculations for Adaptive Cluster Sampling Strategies
  • 25.4. Comparisons with Conventional Systematic and Cluster Sampling
  • 25.5. Derivations
  • 25.6. Example Data
  • Exercises
  • 26. Stratified Adaptive Cluster Sampling
  • 26.1. Designs
  • 26.2. Estimators
  • Estimators Using Expected Numbers of Initial Intersections
  • Estimator Using Initial Intersection Probabilities
  • 26.3. Comparisons with Conventional Stratified Sampling
  • 26.4. Further Improvement of Estimators
  • 26.5. Example Data
  • Exercises
  • Answers to Selected Exercises
  • References
  • Author Index
  • Subject Index
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