Sampling 3E

3rd Auflage
ISBN: 978-0-470-40231-3
Verlag: WILEY

Buch, Englisch, 472 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 868 g

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.

Autoren/Hrsg.

Thompson, Steven K

Fachgebiete

• Geowissenschaften Geographie | Raumplanung Geostatistik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Mathematik Allgemein Diskrete Mathematik, Kombinatorik
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik

Inhaltsverzeichnis

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

Über Autor(innen)

Thompson, Steven K
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).

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).