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Sampling / Steven K. Thompson

版:: 3rd ed
資料種別:: 図書
出版情報:: Hoboken, N.J. : John Wiley & Sons, c2012
形態:: xxi, 436 p. ; 25 cm
シリーズ名:: Wiley series in probability and mathematical statistics <BA00019013>
著者名:: Thompson, Steven K., 1945- <DA07045186>
ISBN:: 9780470402313 [0470402318] (: hardback)
書誌ID:: BB08919037

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注記:

Includes bibliographical references (p. 375-393) and indexes

主題:

Sampling (Statistics)

言語:

英語

目次情報:

Preface

Preface to the Second Edition

Preface to the First Edition

Introduction / 1：

Basic Ideas of Sampling and Estimation / 1.1：

Sampling Units / 1.2：

Sampling and Nonsampling Errors / 1.3：

Models in Sampling / 1.4：

Adaptive and Nonadaptive Designs / 1.5：

Some Sampling History / 1.6：

Basic Sampling / Part I：

Simple Random Sampling / 2：

Selecting a Simple Random Sample / 2.1：

Estimating the Population Mean / 2.2：

Estimating the Population Total / 2.3：

Some Underlying Ideas / 2.4：

Random Sampling with Replacement / 2.5：

Derivations for Random Sampling / 2.6：

Model-Based Approach to Sampling / 2.7：

Computing Notes / 2.8：

Entering Data in R

Sample Estimates

Simulation

Further Comments on the Use of Simulation

Exercises

Confidence Intervals / 3：

Confidence Interval for the Population Mean or Total / 3.1：

Finite-Population Central Limit Theorem / 3.2：

Sampling Distributions / 3.3：

Confidence Interval Computation / 3.4：

Simulations Illustrating the Approximate Normality of a Sampling Distribution with Small n and N

Daily Precipitation Data

Sample Size / 4：

Sample Size for Estimating a Population Mean / 4.1：

Sample Size for Estimating a Population Total / 4.2：

Sample Size for Relative Precision / 4.3：

Estimating Proportions, Ratios, and Subpopulation Means / 5：

Estimating a Population Proportion / 5.1：

Confidence Interval for a Proportion / 5.2：

Sample Size for Estimating a Proportion / 5.3：

Sample Size for Estimating Several Proportions Simultaneously / 5.4：

Estimating a Ratio / 5.5：

Estimating a Mean, Total, or Proportion of a Subpopulation / 5.6：

Estimating a Subpopulation Mean

Estimating a Proportion for a Subpopulation

Estimating a Subpopulation Total

Unequal Probability Sampling / 6：

Sampling with Replacement: The Hansen-Hurwitz Estimator / 6.1：

Any Design: The Horvitz-Thompson Estimator / 6.2：

Generalized Unequal-Probability Estimator / 6.3：

Small Population Example / 6.4：

Derivations and Comments / 6.5：

Writing an R Function to Simulate a Sampling Strategy / 6.6：

Comparing Sampling Strategies

Making The Best Use Of Survey Data / Part II：

Auxiliary Data and Ratio Estimation / 7：

Ratio Estimator / 7.1：

Small Population Illustrating Bias / 7.2：

Derivations and Approximations for the Ratio Estimator / 7.3：

Finite-Population Central Limit Theorem for the Ratio Estimator / 7.4：

Ratio Estimation with Unequal Probability Designs / 7.5：

Models in Ratio Estimation / 7.6：

Types of Estimators for a Ratio

Design Implications of Ratio Models / 7.7：

Regression Estimation / 7.8：

Linear Regression Estimator / 8.1：

Regression Estimation with Unequal Probability Designs / 8.2：

Regression Model / 8.3：

Multiple Regression Models / 8.4：

Design Implications of Regression Models / 8.5：

The Sufficient Statistic in Sampling / 9：

The Set of Distinct, Labeled Observations / 9.1：

Estimation in Random Sampling with Replacement / 9.2：

Estimation in Probability-Proportional-to-Size Sampling / 9.3：

Comments on the Improved Estimates / 9.4：

Design and Model / 10：

Uses of Design and Model in Sampling / 10.1：

Connections between the Design and Model Approaches / 10.2：

Some Comments / 10.3：

Likelihood Function in Sampling / 10.4：

Some Useful Designs / Part III：

Stratified Sampling / 11：

With Any Stratified Design / 11.1：

With Stratified Random Sampling

The Stratification Principle / 11.2：

Allocation in Stratified Random Sampling / 11.5：

Poststratification / 11.6：

Population Model for a Stratified Population / 11.7：

Derivations for Stratified Sampling / 11.8：

Optimum Allocation

Poststratification Variance

Cluster and Systematic Sampling / 11.9：

Primary Units Selected by Simple Random Sampling / 12.1：

Unbiased Estimator

Primary Units Selected with Probabilities Proportional to Size / 12.2：

Hansen-Hurwitz (PPS) Estimator

Horvitz-Thompson Estimator

The Basic Principle / 12.3：

Single Systematic Sample / 12.4：

Variance and Cost in Cluster and Systematic Sampling / 12.5：

Multistage Designs / 12.6：

Simple Random Sampling at Each Stage / 13.1：

Primary Units Selected with Probability Proportional to Size / 13.2：

Any Multistage Design with Replacement / 13.3：

Cost and Sample Sizes / 13.4：

Derivations for Multistage Designs / 13.5：

Probability-Proportional-to-Size Sampling

More Than Two Stages

Double or Two-Phase Sampling / 14：

Ratio Estimation with Double Sampling / 14.1：

Allocation in Double Sampling for Ratio Estimation / 14.2：

Double Sampling for Stratification / 14.3：

Derivations for Double Sampling / 14.4：

Approximate Mean and Variance: Ratio Estimation

Optimum Allocation for Ratio Estimation

Expected Value and Variance: Stratification

Nonsampling Errors and Double Sampling / 14.5：

Nonresponse, Selection Bias, or Volunteer Bias

Double Sampling to Adjust for Nonresponse: Callbacks

Response Modeling and Nonresponse Adjustments

Methods For Elusive And Hard-To-Detect Populations / 14.6：

Network Sampling and Link-Tracing Designs / 15：

Estimation of the Population Total or Mean / 15.1：

Multiplicity Estimator

Stratification in Network Sampling / 15.2：

Other Link-Tracing Designs / 15.4：

Detectability and Sampling / 15.5：

Constant Detectability over a Region / 16.1：

Estimating Detectability / 16.2：

Effect of Estimated Detectability / 16.3：

Detectability with Simple Random Sampling / 16.4：

Estimated Detectability and Simple Random Sampling / 16.5：

Sampling with Replacement / 16.6：

Derivations / 16.7：

Unequal Probability Sampling of Groups with Unequal Detection Probabilities / 16.8：

Line and Point Transects / 16.9：

Density Estimation Methods for Line Transects / 17.1：

Narrow-Strip Method / 17.2：

Smooth-by-Eye Method / 17.3：

Parametric Methods / 17.4：

Nonparametric Methods / 17.5：

Estimating f (0) by the Kernel Method

Fourier Series Method

Designs for Selecting Transects / 17.6：

Random Sample of Transects / 17.7：

Systematic Selection of Transects / 17.8：

Selection with Probability Proportional to Length / 17.9：

Note on Estimation of Variance for the Kernel Method / 17.10：

Some Underlying Ideas about Line Transects / 17.11：

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

Detectability Imperfect on the Line or Dependent on Size / 17.12：

Estimation Using Individual Detectabilities / 17.13：

Estimation of Individual Detectabilities

Detectability Functions other than Line Transects / 17.14：

Variable Circular Plots or Point Transects / 17.15：

Exercise

Capture-Recapture Sampling / 18：

Single Recapture / 18.1：

Models for Simple Capture-Recapture / 18.2：

Sampling Design in Capture-Recapture: Ratio Variance Estimator / 18.3：

Random Sampling with Replacement of Detectability Units

Random Sampling without Replacement

Estimating Detectability with Capture-Recapture Methods / 18.4：

Multiple Releases / 18.5：

More Elaborate Models / 18.6：

Line-Intercept Sampling / 19：

Random Sample of Lines: Fixed Direction / 19.1：

Lines of Random Position and Direction / 19.2：

Spatial Sampling / Part V：

Spatial Prediction or Kriging / 20：

Spatial Covariance Function / 20.1：

Linear Prediction (Kriging) / 20.2：

Variogram / 20.3：

Predicting the Value over a Region / 20.4：

Spatial Designs / 20.5：

Design for Local Prediction / 21.1：

Design for Prediction of Mean of Region / 21.2：

Plot Shapes and Observational Methods / 22：

Observations from Plots / 22.1：

Observations from Detectability Units / 22.2：

Comparisons of Plot Shapes and Detectability Methods / 22.3：

Adaptive Sampling / Part VI：

Adaptive Sampling Designs / 23：

Adaptive and Conventional Designs and Estimators / 23.1：

Brief Survey of Adaptive Sampling / 23.2：

Adaptive Cluster Sampling / 24：

Designs / 24.1：

Initial Simple Random Sample without Replacement

Initial Random Sample with Replacement

Estimators / 24.2：

Initial Sample Mean

Estimation Using Draw-by-Draw Intersections

Estimation Using Initial Intersection Probabilities

When Adaptive Cluster Sampling Is Better than Simple Random Sampling / 24.3：

Expected Sample Size, Cost, and Yield / 24.4：

Comparative Efficiencies of Adaptive and Conventional Sampling / 24.5：

Further Improvement of Estimators / 24.6：

Data for Examples and Figures / 24.7：

Systematic and Strip Adaptive Cluster Sampling / 25：

Estimator Based on Partial Selection Probabilities / 25.1：

Estimator Based on Partial Inclusion Probabilities

Calculations for Adaptive Cluster Sampling Strategies / 25.3：

Comparisons with Conventional Systematic and Cluster Sampling / 25.4：

Example Data / 25.5：

Stratified Adaptive Cluster Sampling / 26：

Estimators Using Expected Numbers of Initial Intersections / 26.1：

Estimator Using Initial Intersection Probabilities

Comparisons with Conventional Stratified Sampling / 26.3：

Answers to Selected Exercises / 26.4：

References

Author Index

Subject Index

Preface

Preface to the Second Edition

Preface to the First Edition

Introduction / 1：

Basic Ideas of Sampling and Estimation / 1.1：

Sampling Units / 1.2：

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