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…mehr
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.
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).
Inhaltsangabe
Preface xv Preface to the Second Edition xvii Preface to the First Edition xix 1 Introduction 1 PART I BASIC SAMPLING 9 2 Simple Random Sampling 11 Entering Data in R 26 Sample Estimates 27 Simulation 28 Further Comments on the Use of Simulation 32 Exercises 35 3 Confidence Intervals 39 Confidence Interval Computation 44 Simulations Illustrating the Approximate Normality of a Sampling Distribution with Small n and N 45 Daily Precipitation Data 46 Exercises 50 4 Sample Size 53 Exercises 56 5 Estimating Proportions Ratios and Subpopulation Means 57 Estimating a Subpopulation Mean 63 Estimating a Proportion for a Subpopulation 64 Estimating a Subpopulation Total 64 Exercises 65 6 Unequal Probability Sampling 67 Writing an R Function to Simulate a Sampling Strategy 82 Comparing Sampling Strategies 84 Exercises 88 PART II MAKING THE BEST USE OF SURVEY DATA 91 7 Auxiliary Data and Ratio Estimation 93 Types of Estimators for a Ratio 109 Exercises 112 8 Regression Estimation 115 Exercises 124 9 The Sufficient Statistic in Sampling 125 10 Design and Model 131 PART III SOME USEFUL DESIGNS 139 11 Stratified Sampling 141 With Any Stratified Design 142 With Stratified Random Sampling 143 With Any Stratified Design 144 With Stratified Random Sampling 144 Optimum Allocation 149 Poststratification Variance 150 Exercises 155 12 Cluster and Systematic Sampling 157 Unbiased Estimator 159 Ratio Estimator 160 Hansen-Hurwitz (PPS) Estimator 161 Horvitz-Thompson Estimator 161 Exercises 169 13 Multistage Designs 171 Unbiased Estimator 173 Ratio Estimator 175 Unbiased Estimator 179 Ratio Estimator 181 Probability-Proportional-to-Size Sampling 181 More Than Two Stages 181 Exercises 182 14 Double or Two-Phase Sampling 183 Approximate Mean and Variance: Ratio Estimation 188 Optimum Allocation for Ratio Estimation 189 Expected Value and Variance: Stratification 189 Nonresponse Selection Bias or Volunteer Bias 191 Double Sampling to Adjust for Nonresponse: Callbacks 192 Response Modeling and Nonresponse Adjustments 193 Exercises 197 PART IV METHODS FOR ELUSIVE AND HARD-TO-DETECT POPULATIONS 199 15 Network Sampling and Link-Tracing Designs 201 Multiplicity Estimator 202 Horvitz-Thompson Estimator 204 Exercises 213 16 Detectability and Sampling 215 Exercises 227 17 Line and Point Transects 229 Estimating f (0) by the Kernel Method 237 Fourier Series Method 239 Unbiased Estimator 241 Ratio Estimator 243 Line Transects and Detectability Functions 247 Single Transect 249 Average Detectability 249 Random Transect 250 Average Detectability and Effective Area 251 Effect of Estimating Detectability 252 Probability Density Function of an Observed Distance 253 Estimation of Individual Detectabilities 256 Exercise 260 18 Capture-Recapture Sampling 263 Random Sampling with Replacement of Detectability Units 269 Random Sampling without Replacement 270 Exercise 273 19 Line-Intercept Sampling 275 Exercises 282 PART V SPATIAL SAMPLING 283 20 Spatial Prediction or Kriging 285 Exercise 299 21 Spatial Designs 301 22 Plot Shapes and Observational Methods 305 PART VI ADAPTIVE SAMPLING 313 23 Adaptive Sampling Designs 315 24 Adaptive Cluster Sampling 319 Initial Simple Random Sample without Replacement 322 Initial Random Sample with Replacement 323 Initial Sample Mean 323 Estimation Using Draw-by-Draw Intersections 323 Estimation Using Initial Intersection Probabilities 325 Sampling 328 Exercises 337 25 Systematic and Strip Adaptive Cluster Sampling 339 Initial Sample Mean 343 Estimator Based on Partial Selection Probabilities 344 Estimator Based on Partial Inclusion Probabilities 345 Exercises 352 26 Stratified Adaptive Cluster Sampling 353 Estimators Using Expected Numbers of Initial Intersections 357 Estimator Using Initial Intersection Probabilities 359 Exercises 367 Answers to Selected Exercises 369 References 375 Author Index 395 Subject Index 399
Preface xv Preface to the Second Edition xvii Preface to the First Edition xix 1 Introduction 1 PART I BASIC SAMPLING 9 2 Simple Random Sampling 11 Entering Data in R 26 Sample Estimates 27 Simulation 28 Further Comments on the Use of Simulation 32 Exercises 35 3 Confidence Intervals 39 Confidence Interval Computation 44 Simulations Illustrating the Approximate Normality of a Sampling Distribution with Small n and N 45 Daily Precipitation Data 46 Exercises 50 4 Sample Size 53 Exercises 56 5 Estimating Proportions Ratios and Subpopulation Means 57 Estimating a Subpopulation Mean 63 Estimating a Proportion for a Subpopulation 64 Estimating a Subpopulation Total 64 Exercises 65 6 Unequal Probability Sampling 67 Writing an R Function to Simulate a Sampling Strategy 82 Comparing Sampling Strategies 84 Exercises 88 PART II MAKING THE BEST USE OF SURVEY DATA 91 7 Auxiliary Data and Ratio Estimation 93 Types of Estimators for a Ratio 109 Exercises 112 8 Regression Estimation 115 Exercises 124 9 The Sufficient Statistic in Sampling 125 10 Design and Model 131 PART III SOME USEFUL DESIGNS 139 11 Stratified Sampling 141 With Any Stratified Design 142 With Stratified Random Sampling 143 With Any Stratified Design 144 With Stratified Random Sampling 144 Optimum Allocation 149 Poststratification Variance 150 Exercises 155 12 Cluster and Systematic Sampling 157 Unbiased Estimator 159 Ratio Estimator 160 Hansen-Hurwitz (PPS) Estimator 161 Horvitz-Thompson Estimator 161 Exercises 169 13 Multistage Designs 171 Unbiased Estimator 173 Ratio Estimator 175 Unbiased Estimator 179 Ratio Estimator 181 Probability-Proportional-to-Size Sampling 181 More Than Two Stages 181 Exercises 182 14 Double or Two-Phase Sampling 183 Approximate Mean and Variance: Ratio Estimation 188 Optimum Allocation for Ratio Estimation 189 Expected Value and Variance: Stratification 189 Nonresponse Selection Bias or Volunteer Bias 191 Double Sampling to Adjust for Nonresponse: Callbacks 192 Response Modeling and Nonresponse Adjustments 193 Exercises 197 PART IV METHODS FOR ELUSIVE AND HARD-TO-DETECT POPULATIONS 199 15 Network Sampling and Link-Tracing Designs 201 Multiplicity Estimator 202 Horvitz-Thompson Estimator 204 Exercises 213 16 Detectability and Sampling 215 Exercises 227 17 Line and Point Transects 229 Estimating f (0) by the Kernel Method 237 Fourier Series Method 239 Unbiased Estimator 241 Ratio Estimator 243 Line Transects and Detectability Functions 247 Single Transect 249 Average Detectability 249 Random Transect 250 Average Detectability and Effective Area 251 Effect of Estimating Detectability 252 Probability Density Function of an Observed Distance 253 Estimation of Individual Detectabilities 256 Exercise 260 18 Capture-Recapture Sampling 263 Random Sampling with Replacement of Detectability Units 269 Random Sampling without Replacement 270 Exercise 273 19 Line-Intercept Sampling 275 Exercises 282 PART V SPATIAL SAMPLING 283 20 Spatial Prediction or Kriging 285 Exercise 299 21 Spatial Designs 301 22 Plot Shapes and Observational Methods 305 PART VI ADAPTIVE SAMPLING 313 23 Adaptive Sampling Designs 315 24 Adaptive Cluster Sampling 319 Initial Simple Random Sample without Replacement 322 Initial Random Sample with Replacement 323 Initial Sample Mean 323 Estimation Using Draw-by-Draw Intersections 323 Estimation Using Initial Intersection Probabilities 325 Sampling 328 Exercises 337 25 Systematic and Strip Adaptive Cluster Sampling 339 Initial Sample Mean 343 Estimator Based on Partial Selection Probabilities 344 Estimator Based on Partial Inclusion Probabilities 345 Exercises 352 26 Stratified Adaptive Cluster Sampling 353 Estimators Using Expected Numbers of Initial Intersections 357 Estimator Using Initial Intersection Probabilities 359 Exercises 367 Answers to Selected Exercises 369 References 375 Author Index 395 Subject Index 399
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