Sampling /

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Bibliographic Details
Author / Creator:Thompson, Steven K., 1945-
Edition:2nd ed.
Imprint:New York : Wiley, c2002.
Description:xvii, 367 p. : ill. ; 24 cm.
Language:English
Series: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/4663813
Hidden Bibliographic Details
ISBN:0471291161 (acid-free paper)
Notes:"A Wiley-Interscience publication."
Includes bibliographical references (p. 343-360) and index.
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
  • Exercises
  • 3.. Confidence Intervals
  • 3.1. Confidence Interval for the Population Mean or Total
  • 3.2. Finite-Population Central Limit Theorem
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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