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Steven K. Thompson, PhD, is Shrum Chair in Science and Professor of Statistics at the Simon Fraser University. During his career, he has served on the faculties of the Pennsylvania State University, the University of Auckland, and the University of Alaska. He is also the coauthor of Adaptive Sampling (Wiley).
Preface | p. xv |
Preface to the Second Edition | p. xvii |
Preface to the First Edition | p. xix |
Introduction | p. 1 |
Basic Ideas of Sampling and Estimation | p. 2 |
Sampling Units | p. 4 |
Sampling and Nonsampling Errors | p. 5 |
Models in Sampling | p. 5 |
Adaptive and Nonadaptive Designs | p. 6 |
Some Sampling History | p. 7 |
Basic Sampling | p. 9 |
Simple Random Sampling | p. 11 |
Selecting a Simple Random Sample | p. 11 |
Estimating the Population Mean | p. 13 |
Estimating the Population Total | p. 16 |
Some Underlying Ideas | p. 17 |
Random Sampling with Replacement | p. 19 |
Derivations for Random Sampling | p. 20 |
Model-Based Approach to Sampling | p. 22 |
Computing Notes | p. 26 |
Entering Data in R | p. 26 |
Sample Estimates | p. 27 |
Simulation | p. 28 |
Further Comments on the Use of Simulation | p. 32 |
Exercises | p. 35 |
Confidence Intervals | p. 39 |
Confidence Interval for the Population Mean or Total | p. 39 |
Finite-Population Central Limit Theorem | p. 41 |
Sampling Distributions | p. 43 |
Computing Notes | p. 44 |
Confidence Interval Computation | p. 44 |
Simulations Illustrating the Approximate Normality of a Sampling Distribution with Small n and N | p. 45 |
Daily Precipitation Data | p. 46 |
Exercises | p. 50 |
Sample Size | p. 53 |
Sample Size for Estimating a Population Mean | p. 54 |
Sample Size for Estimating a Population Total | p. 54 |
Sample Size for Relative Precision | p. 55 |
Exercises | p. 56 |
Estimating Proportions, Ratios, and Subpopulation Means | p. 57 |
Estimating a Population Proportion | p. 58 |
Confidence Interval for a Proportion | p. 58 |
Sample Size for Estimating a Proportion | p. 59 |
Sample Size for Estimating Several Proportions Simultaneously | p. 60 |
Estimating a Ratio | p. 62 |
Estimating a Mean, Total, or Proportion of a Subpopulation | p. 62 |
Estimating a Subpopulation Mean | p. 63 |
Estimating a Proportion for a Subpopulation | p. 64 |
Estimating a Subpopulation Total | p. 64 |
Exercises | p. 65 |
Unequal Probability Sampling | p. 67 |
Sampling with Replacement: The Hansen-Hurwitz Estimator | p. 67 |
Any Design: The Horvitz-Thompson Estimator | p. 69 |
Generalized Unequal-Probability Estimator | p. 72 |
Small Population Example | p. 73 |
Derivations and Comments | p. 75 |
Computing Notes | p. 78 |
Writing an R Function to Simulate a Sampling Strategy | p. 82 |
Comparing Sampling Strategies | p. 84 |
Exercises | p. 88 |
Making The Best Use Of Survey Data | p. 91 |
Auxiliary Data and Ratio Estimation | p. 93 |
Ratio Estimator | p. 94 |
Small Population Illustrating Bias | p. 97 |
Derivations and Approximations for the Ratio Estimator | p. 99 |
Finite-Population Central Limit Theorem for the Ratio Estimator | p. 101 |
Ratio Estimation with Unequal Probability Designs | p. 102 |
Models in Ratio Estimation | p. 105 |
Types of Estimators for a Ratio | p. 109 |
Design Implications of Ratio Models | p. 109 |
Computing Notes | p. 110 |
Exercises | p. 112 |
Regression Estimation | p. 115 |
Linear Regression Estimator | p. 116 |
Regression Estimation with Unequal Probability Designs | p. 118 |
Regression Model | p. 119 |
Multiple Regression Models | p. 120 |
Design Implications of Regression Models | p. 123 |
Exercises | p. 124 |
The Sufficient Statistic in Sampling | p. 125 |
The Set of Distinct, Labeled Observations | p. 125 |
Estimation in Random Sampling with Replacement | p. 126 |
Estimation in Probability-Proportional-to-Size Sampling | p. 127 |
Comments on the Improved Estimates | p. 128 |
Design and Model | p. 131 |
Uses of Design and Model in Sampling | p. 131 |
Connections between the Design and Model Approaches | p. 132 |
Some Comments | p. 134 |
Likelihood Function in Sampling | p. 135 |
Some Useful Designs | p. 139 |
Stratified Sampling | p. 141 |
Estimating the Population Total | p. 142 |
With Any Stratified Design | p. 142 |
With Stratified Random Sampling | p. 143 |
Estimating the Population Mean | p. 144 |
With Any Stratified Design | p. 144 |
With Stratified Random Sampling | p. 144 |
Confidence Intervals | p. 145 |
The Stratification Principle | p. 146 |
Allocation in Stratified Random Sampling | p. 146 |
Poststratification | p. 148 |
Population Model for a Stratified Population | p. 149 |
Derivations for Stratified Sampling | p. 149 |
Optimum Allocation | p. 149 |
Poststratification Variance | p. 150 |
Computing Notes | p. 151 |
Exercises | p. 155 |
Cluster and Systematic Sampling | p. 157 |
Primary Units Selected by Simple Random Sampling | p. 159 |
Unbiased Estimator | p. 159 |
Ratio Estimator | p. 160 |
Primary Units Selected with Probabilities Proportional to Size | p. 161 |
Hansen-Hurwitz (PPS) Estimator | p. 161 |
Horvitz-Thompson Estimator | p. 161 |
The Basic Principle | p. 162 |
Single Systematic Sample | p. 162 |
Variance and Cost in Cluster and Systematic Sampling | p. 163 |
Computing Notes | p. 166 |
Exercises | p. 169 |
Multistage Designs | p. 171 |
Simple Random Sampling at Each Stage | p. 173 |
Unbiased Estimator | p. 173 |
Ratio Estimator | p. 175 |
Primary Units Selected with Probability Proportional to Size | p. 176 |
Any Multistage Design with Replacement | p. 177 |
Cost and Sample Sizes | p. 177 |
Derivations for Multistage Designs | p. 179 |
Unbiased Estimator | p. 179 |
Ratio Estimator | p. 181 |
Probability-Proportional-to-Size Sampling | p. 181 |
More Than Two Stages | p. 181 |
Exercises | p. 182 |
Double or Two-Phase Sampling | p. 183 |
Ratio Estimation with Double Sampling | p. 184 |
Allocation in Double Sampling for Ratio Estimation | p. 186 |
Double Sampling for Stratification | p. 186 |
Derivations for Double Sampling | p. 188 |
Approximate Mean and Variance: Ratio Estimation | p. 188 |
Optimum Allocation for Ratio Estimation | p. 189 |
Expected Value and Variance: Stratification | p. 189 |
Nonsampling Errors and Double Sampling | p. 190 |
Nonresponse, Selection Bias, or Volunteer Bias | p. 191 |
Double Sampling to Adjust for Nonresponse: Callbacks | p. 192 |
Response Modeling and Nonresponse Adjustments | p. 193 |
Computing Notes | p. 195 |
Exercises | p. 197 |
Methods For Elusive And Hard-To-Detect Populations | p. 199 |
Network Sampling and Link-Tracing Designs | p. 201 |
Estimation of the Population Total or Mean | p. 202 |
Multiplicity Estimator | p. 202 |
Horvitz-Thompson Estimator | p. 204 |
Derivations and Comments | p. 207 |
Stratification in Network Sampling | p. 208 |
Other Link-Tracing Designs | p. 210 |
Computing Notes | p. 212 |
Exercises | p. 213 |
Detectability and Sampling | p. 215 |
Constant Detectability over a Region | p. 215 |
Estimating Detectability | p. 217 |
Effect of Estimated Detectability | p. 218 |
Detectability with Simple Random Sampling | p. 219 |
Estimated Detectability and Simple Random Sampling | p. 220 |
Sampling with Replacement | p. 222 |
Derivations | p. 222 |
Unequal Probability Sampling of Groups with Unequal Detection Probabilities | p. 224 |
Derivations | p. 225 |
Exercises | p. 227 |
Line and Point Transects | p. 229 |
Density Estimation Methods for Line Transects | p. 230 |
Narrow-Strip Method | p. 230 |
Smooth-by-Eye Method | p. 233 |
Parametric Methods | p. 234 |
Nonparametric Methods | p. 237 |
Estimating f (0) by the Kernel Method | p. 237 |
Fourier Series Method | p. 239 |
Designs for Selecting Transects | p. 240 |
Random Sample of Transects | p. 240 |
Unbiased Estimator | p. 241 |
Ratio Estimator | p. 243 |
Systematic Selection of Transects | p. 244 |
Selection with Probability Proportional to Length | p. 244 |
Note on Estimation of Variance for the Kernel Method | p. 246 |
Some Underlying Ideas about Line Transects | p. 247 |
Line Transects and Detectability Functions | p. 247 |
Single Transect | p. 249 |
Average Detectability | p. 249 |
Random Transect | p. 250 |
Average Detectability and Effective Area | p. 251 |
Effect of Estimating Detectability | p. 252 |
Probability Density Function of an Observed Distance | p. 253 |
Detectability Imperfect on the Line or Dependent on Size | p. 255 |
Estimation Using Individual Detectabilities | p. 255 |
Estimation of Individual Detectabilities | p. 256 |
Detectability Functions other than Line Transects | p. 257 |
Variable Circular Plots or Point Transects | p. 259 |
Exercise | p. 260 |
Capture-Recapture Sampling | p. 263 |
Single Recapture | p. 264 |
Models for Simple Capture-Recapture | p. 266 |
Sampling Design in Capture-Recapture: Ratio Variance Estimator | p. 267 |
Random Sampling with Replacement of Detectability Units | p. 269 |
Random Sampling without Replacement | p. 270 |
Estimating Detectability with Capture-Recapture Methods | p. 271 |
Multiple Releases | p. 272 |
More Elaborate Models | p. 273 |
Exercise | p. 273 |
Line-Intercept Sampling | p. 275 |
Random Sample of Lines: Fixed Direction | p. 275 |
Lines of Random Position and Direction | p. 280 |
Exercises | p. 282 |
Spatial Sampling | p. 283 |
Spatial Prediction or Kriging | p. 285 |
Spatial Covariance Function | p. 286 |
Linear Prediction (Kriging) | p. 286 |
Variogram | p. 289 |
Predicting the Value over a Region | p. 291 |
Derivations and Comments | p. 292 |
Computing Notes | p. 296 |
Exercise | p. 299 |
Spatial Designs | p. 301 |
Design for Local Prediction | p. 302 |
Design for Prediction of Mean of Region | p. 302 |
Plot Shapes and Observational Methods | p. 305 |
Observations from Plots | p. 305 |
Observations from Detectability Units | p. 307 |
Comparisons of Plot Shapes and Detectability Methods | p. 308 |
Adaptive Sampling | p. 313 |
Adaptive Sampling Designs | p. 315 |
Adaptive and Conventional Designs and Estimators | p. 315 |
Brief Survey of Adaptive Sampling | p. 316 |
Adaptive Cluster Sampling | p. 319 |
Designs | p. 321 |
Initial Simple Random Sample without Replacement | p. 322 |
Initial Random Sample with Replacement | p. 323 |
Estimators | p. 323 |
Initial Sample Mean | p. 323 |
Estimation Using Draw-by-Draw Intersections | p. 323 |
Estimation Using Initial Intersection Probabilities | p. 325 |
When Adaptive Cluster Sampling Is Better than Simple Random Sampling | p. 327 |
Expected Sample Size, Cost, and Yield | p. 328 |
Comparative Efficiencies of Adaptive and Conventional Sampling | p. 328 |
Further Improvement of Estimators | p. 330 |
Derivations | p. 333 |
Data for Examples and Figures | p. 336 |
Exercises | p. 337 |
Systematic and Strip Adaptive Cluster Sampling | p. 339 |
Designs | p. 341 |
Estimators | p. 343 |
Initial Sample Mean | p. 343 |
Estimator Based on Partial Selection Probabilities | p. 344 |
Estimator Based on Partial Inclusion Probabilities | p. 345 |
Calculations for Adaptive Cluster Sampling Strategies | p. 347 |
Comparisons with Conventional Systematic and Cluster Sampling | p. 349 |
Derivations | p. 350 |
Example Data | p. 352 |
Exercises | p. 352 |
Stratified Adaptive Cluster Sampling | p. 353 |
Designs | p. 353 |
Estimators | p. 356 |
Estimators Using Expected Numbers of Initial Intersections | p. 357 |
Estimator Using Initial Intersection Probabilities | p. 359 |
Comparisons with Conventional Stratified Sampling | p. 362 |
Further Improvement of Estimators | p. 364 |
Example Data | p. 367 |
Exercises | p. 367 |
Answers to Selected Exercises | p. 369 |
References | p. 375 |
Author Index | p. 395 |
Subject Index | p. 399 |
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