Sampling / Edition 3

Sampling / Edition 3

by Steven K. Thompson

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ISBN-10: 0470402318

ISBN-13: 9780470402313

Pub. Date: 03/13/2012

Publisher: Wiley

Praise for the Second Edition

"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

"Well-written . . . an excellent book on an important subject. Highly recommended." —Choice

"An ideal reference for


Praise for the Second Edition

"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

"Well-written . . . an excellent book on an important subject. Highly recommended." —Choice

"An ideal reference for scientific researchers and other professionals who use sampling." —Zentralblatt Math

Features new developments in the field combined with all aspects of obtaining, interpreting, and using sample data

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 Third Edition retains the general organization of the two previous editions, but incorporates extensive new material—sections, exercises, and examples—throughout. Inside, readers will find all-new approaches to explain the various techniques in the book; new figures to assist in better visualizing and comprehending underlying concepts such as the different sampling strategies; computing notes for sample selection, calculation of estimates, and simulations; and more.

Organized into six sections, the book covers basic sampling, from simple random to unequal probability sampling; the use of auxiliary data with ratio 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.

Featuring a broad range of topics, Sampling, Third Edition serves as a valuable reference on useful sampling and estimation methods for researchers in various fields of study, including biostatistics, ecology, and the health sciences. The book is also ideal for courses on statistical sampling at the upper-undergraduate and graduate levels.

Product Details

Publication date:
CourseSmart Series, #755
Edition description:
New Edition
Product dimensions:
6.30(w) x 10.30(h) x 1.20(d)

Related Subjects

Table of Contents

Preface to the Second Editionxiii
Preface to the First Editionxv
1.1Basic Ideas of Sampling and Estimation2
1.2Sampling Units4
1.3Sampling and Nonsampling Errors5
1.4Models in Sampling5
1.5Adaptive and Nonadaptive Designs6
1.6Some Sampling History7
Part IBasic Sampling9
2.Simple Random Sampling11
2.1Selecting a Simple Random Sample11
2.2Estimating the Population Mean13
2.3Estimating the Population Total16
2.4Some Underlying Ideas17
2.5Random Sampling with Replacement19
2.6Derivations for Random Sampling20
2.7Model-Based Approach to Sampling22
3.Confidence Intervals29
3.1Confidence Interval for the Population Mean or Total29
3.2Finite-Population Central Limit Theorem30
4.Sample Size35
4.1Sample Size for Estimating a Population Mean36
4.2Sample Size for Estimating a Population Total36
4.3Sample Size for Relative Precision37
5.Estimating Proportions, Ratios, and Subpopulation Means39
5.1Estimating a Population Proportion39
5.2Confidence Interval for a Proportion40
5.3Sample Size for Estimating a Proportion41
5.4Sample Size for Estimating Several Proportions Simultaneously42
5.5Estimating a Ratio44
5.6Estimating a Mean, Total, or Proportion of a Subpopulation45
Estimating a Subpopulation Mean45
Estimating a Proportion for a Subpopulation46
Estimating a Subpopulation Total47
6.Unequal Probability Sampling51
6.1Sampling with Replacement: The Hansen--Hurwitz Estimator51
6.2Any Design: The Horvitz--Thompson Estimator53
6.3Generalized Unequal-Probability Estimator56
6.4Small Population Example57
6.5Derivations and Comments60
Part IIMaking the Best Use of Survey Data65
7.Auxiliary Data and Ratio Estimation67
7.1Ratio Estimator68
7.2Small Population Illustrating Bias72
7.3Derivations and Approximations for the Ratio Estimator73
7.4Finite-Population Central Limit Theorem for the Ratio Estimator76
7.5Ratio Estimation with Unequal Probability Designs76
7.6Models in Ratio Estimation79
Types of Estimators for a Ratio83
7.7Design Implications of Ratio Models84
8.Regression Estimation89
8.1Linear Regression Estimator90
8.2Regression Estimation with Unequal Probability Designs92
8.3Regression Model93
8.4Multiple Regression Models94
8.5Design Implications of Regression Models97
9.The Sufficient Statistic in Sampling101
9.1The Set of Distinct, Labeled Observations101
9.2Estimation in Random Sampling with Replacement102
9.3Estimation in Probability-Proportional-to-Size Sampling103
9.4Comments on the Improved Estimates104
10.Design and Model107
10.1Uses of Design and Model in Sampling107
10.2Connections between the Design and Model Approaches108
10.3Some Comments110
10.4Likelihood Function in Sampling111
Part IIISome Useful Designs115
11.Stratified Sampling117
11.1Estimating the Population Total118
With Any Stratified Design118
With Stratified Random Sampling119
11.2Estimating the Population Mean120
With Any Stratified Design120
With Stratified Random Sampling120
11.3Confidence Intervals121
11.4The Stratification Principle122
11.5Allocation in Stratified Random Sampling122
11.7Population Model for a Stratified Population125
11.8Derivations for Stratified Sampling126
Optimum Allocation126
Poststratification Variance127
12.Cluster and Systematic Sampling129
12.1Primary Units Selected by Simple Random Sampling131
Unbiased Estimator131
Ratio Estimator132
12.2Primary Units Selected with Probabilities Proportional to Size133
Hansen--Hurwitz (PPS) Estimator133
Horvitz--Thompson Estimator134
12.3The Basic Principle134
12.4Single Systematic Sample135
12.5Variance and Cost in Cluster and Systematic Sampling136
13.Multistage Designs143
13.1Simple Random Sampling at Each Stage145
Unbiased Estimator145
Ratio Estimator147
13.2Primary Units Selected with Probability Proportional to Size148
13.3Any Multistage Design with Replacement149
13.4Cost and Sample Sizes150
13.5Derivations for Multistage Designs151
Unbiased Estimator152
Ratio Estimator153
Probability-Proportional-to-Size Sampling153
More Than Two Stages154
14.Double or Two-Phase Sampling157
14.1Ratio Estimation with Double Sampling158
14.2Allocation in Double Sampling for Ratio Estimation160
14.3Double Sampling for Stratification160
14.4Derivations for Double Sampling162
Approximate Mean and Variance: Ratio Estimation162
Optimum Allocation for Ratio Estimation163
Expected Value and Variance: Stratification164
14.5Nonsampling Errors and Double Sampling165
Nonresponse, Selection Bias, or Volunteer Bias166
Double Sampling to Adjust for Nonresponse: Callbacks166
Response Modeling and Nonresponse Adjustments167
Part IVMethods for Elusive and Hard-to-Detect Populations171
15.Network Sampling and Link-Tracing Designs173
15.1Estimation of the Population Total or Mean174
Multiplicity Estimator174
Horvitz--Thompson Estimator176
15.2Derivations and Comments179
15.3Stratification in Network Sampling180
15.4Other Link-Tracing Designs182
16.Detectability and Sampling185
16.1Constant Detectability over a Region185
16.2Estimating Detectability187
16.3Effect of Estimated Detectability188
16.4Detectability with Simple Random Sampling189
16.5Estimated Detectability and Simple Random Sampling191
16.6Sampling with Replacement192
16.8Unequal Probability Sampling of Groups with Unequal Detection Probabilities194
17.Line and Point Transects199
17.1Density Estimation Methods for Line Transects200
17.2Narrow-Strip Method201
17.3Smooth-by-Eye Method203
17.4Parametric Methods204
17.5Nonparametric Methods207
Estimating f(0) by the Kernel Method207
Fourier Series Method209
17.6Designs for Selecting Transects210
17.7Random Sample of Transects211
Unbiased Estimator212
Ratio Estimator213
17.8Systematic Selection of Transects214
17.9Selection with Probability Proportional to Length215
17.10Note on Estimation of Variance for the Kernel Method216
17.11Some Underlying Ideas about Line Transects218
Line Transects and Detectability Functions218
Single Transect219
Average Detectability220
Random Transect220
Average Detectability and Effective Area222
Effect of Estimating Detectability223
Probability Density Function of an Observed Distance223
17.12Detectability Imperfect on the Line or Dependent on Size226
17.13Estimation Using Individual Detectabilities226
Estimation of Individual Detectabilities227
17.14Detectability Functions Other Than Line Transects228
17.15Variable Circular Plots or Point Transects230
18.Capture--Recapture Sampling233
18.1Single Recapture234
18.2Models for Simple Capture--Recapture236
18.3Sampling Design in Capture--Recapture: Ratio Variance Estimator237
Random Sampling with Replacement of Detectability Units239
Random Sampling without Replacement240
18.4Estimating Detectability with Capture--Recapture Methods241
18.5Multiple Releases242
18.6More Elaborate Models243
19.Line-Intercept Sampling245
19.1Random Sample of Lines: Fixed Direction245
19.2Lines of Random Position and Direction250
Part VSpatial Sampling255
20.Spatial Prediction or Kriging257
20.1Spatial Covariance Function258
20.2Linear Prediction (Kriging)258
20.4Predicting the Value over a Region264
20.5Derivations and Comments265
21.Spatial Designs271
21.1Design for Local Prediction272
21.2Design for Prediction of Mean of Region272
22.Plot Shapes and Observational Methods275
22.1Observations from Plots275
22.2Observations from Detectability Units277
22.3Comparisons of Plot Shapes and Detectability Methods278
Part VIAdaptive Sampling283
23.Adaptive Sampling Designs285
23.1Adaptive and Conventional Designs and Estimators285
23.2Brief Survey of Adaptive Sampling286
24.Adaptive Cluster Sampling289
Initial Simple Random Sample without Replacement292
Initial Random Sample with Replacement293
Initial Sample Mean293
Estimation Using Draw-by-Draw Intersections293
Estimation Using Initial Intersection Probabilities295
24.3When Adaptive Cluster Sampling Is Better Than Simple Random Sampling297
24.4Expected Sample Size, Cost, and Yield298
24.5Comparative Efficiencies of Adaptive and Conventional Sampling298
24.6Further Improvement of Estimators300
24.8Data for Examples and Figures306
25.Systematic and Strip Adaptive Cluster Sampling309
Initial Sample Mean313
Estimator Based on Partial Selection Probabilities314
Estimator Based on Partial Inclusion Probabilities316
25.3Calculations for Adaptive Cluster Sampling Strategies317
25.4Comparisons with Conventional Systematic and Cluster Sampling319
25.6Example Data322
26.Stratified Adaptive Cluster Sampling323
Estimators Using Expected Numbers of Initial Intersections327
Estimator Using Initial Intersection Probabilities329
26.3Comparisons with Conventional Stratified Sampling332
26.4Further Improvement of Estimators334
26.5Example Data338
Answers to Selected Exercises339
Author Index361
Subject Index365

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