抽样理论与方法 英文版【2025|PDF|Epub|mobi|kindle电子书版本百度云盘下载】

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1 PRELIMINARIES1

1.1 Introduction1

1.2 A Brief History of Survey Sampling1

1.3 Sampling Designs and an Overview of Sampling3

1.4 Ingredients of a Survey3

1.5 Probability Sampling4

1.6 Precision and Confidence Intervals5

1.7 Biased Estimators5

1.8 The Mean-Squared Error6

1.9 Unbiased Estimation7

PROBLEMS8

REFERENCES9

2 VARYING-PROBABILITY SAMPLING11

2.1 Introduction11

2.2 Obtaining Varying-Probability Samples11

2.3 Sampling Designs(Ordered and Unordered)15

2.4 Sufficiency in Sampling from Finite Populations19

2.5 Sampling with Varying Probabilities and Without Replacement23

PROBLEMS26

REFERENCES27

3 SIMPLE RANDOM SAMPLING28

3.1 Introduction28

3.2 Notation29

3.3 Properties of Estimates30

3.4 Variances of Estimators31

3.5 Confidence Intervals33

3.6 Alternate Method for Evaluating var(？)34

3.7 Random Sampling with Replacement35

3.8 Estimates for Ratios36

3.9 Estimates of Means or Totals over Subpopulations38

3.10 Justification of the Normal Approximation38

3.11 Asymptotic Normality of Estimates Arising from Simple Random Sampling38

3.12 Best Unbiased Estimators40

3.13 Distinct Units42

3.14 The Distribution of W44

3.15 Comparison of Simple Random Sampling with and without Replacement49

3.16 Use of Balanced Incomplete Block Designs in Simple Random Sampling51

3.17 Estimating Proportions and Percentages52

3.18 Binomial and Hypergeometric Distributions and Their Use in Sampling54

3.19 Confidence Limits for M55

3.20 Confidence Intervals for Unknown Discrete Population Parameter55

3.21 Use of the Finite Population Correction for Binomial Confidence Limits57

3.22 Cluster Sampling:Estimation of Proportions57

PROBLEMS59

REFERENCES63

4 ESTIMATION OFTHE SAMPLE SIZE64

4.1 Introduction64

4.2 Sample Size in Estimating Proportions64

4.3 Inverse Sampling for Rare Attributes66

4.4 Estimating Sample Size with Continuous Data68

4.5 Estimation of S268

4.6 Estimation by Double Sampling69

4.7 Estimation with Given Variance:Single Unknown Parameter69

4.8 Sampling Procedure69

4.9 Estimation of P with Specified Variance V70

4.10 Estimation of P with Specified CV=C1/271

4.11 Estimation of ？with Specified CV=C1/271

4.12 Estimation of ？ with Specified Variance V71

4.13 Computing Sample Size:Decision-Theoretic Approach72

PROBLEMS73

REFERENCES74

5 STRATIFIED SAMPLING75

5.1 Introduction75

5.2 Estimators of Mean and Total and Their Properties76

5.3 Confidence Limits(CI's)78

5.4 Optimum Allocation of a Random Sample79

5.5 Merits of Stratified Sampling(SS)Relative to Simple Random Sampling(SRS)82

5.6 Modification of Optimal Allocation84

5.7 Estimation of Sample Sizes in Stratified Sampling:Continuous Response Data85

5.8 Estimation of the Population Mean ？85

5.9 Estimation of the Population Total86

5.10 Application to Stratified Sampling for Proportions86

5.11 Minimum Variance for Fxed n(Total Sample Size)87

5.12 Gain by Stratified Sampling for Proportions87

5.13 Sample Size for Proportions88

5.14 Poststratification89

5.15 How Should the Strata be Formed?292

5.16 Optimal Choice of L and n100

5.17 Optimal Choice of L and n Via a Regression Variable101

5.18 Controlled Sampling103

5.19 Multiple Stratification104

5.20 Interpenetrating Subsampling105

PROBLEMS108

REFERENCES114

6 RATIO ESTIMATORS116

6.1 Introduction116

6.2 Variance of the Ratio Estimate117

6.3 Estimates for var(？R)117

6.4 Confidence Intervals for R118

6.5 Efficiency Comparisons118

6.6 An Optimum Property of the Ratio Estimators120

6.7 Bias in the Ratio Estimate123

6.8 An Exact Expression for the Bias of the Ratio Estimate124

6.9 Ratio Estimates in Stratified Random Sampling125

6.10 Comparison of ？Rs and ？Rc126

6.11 Optimum Allocation with a Ratio Estimator127

6.12 Unbiased Ratio Estimates128

6.13 Jackknife Method for Obtaining a Ratio Estimate with Bias O(n-2)128

6.14 Multivariate Ratio Estimators130

6.15 A Dual Ratio Estimator131

6.16 Comparison of Various Estimators132

6.17 Unbiased Ratio Estimator134

PROBLEMS134

REFERENCES142

7 REGRESSION ESTIMATORS143

7.1 Introduction143

7.2 Properties of Regression Estimators144

7.3 Sample Estimate of Variance147

7.4 Comparison of Regression,Ratio Estimates,and the Sample Mean147

7.5 Properties of the Regression Estimator under a Super Population Model149

7.6 Regression Estimates in Stratiffed Sampling150

7.7 Sample Estimates151

7.8 Unbiased Regression Estimation154

PROBLEMS156

REFERENCES161

8 SYSTEMATIC SAMPLING162

8.1 Circular Systematic Sampling162

8.2 Relation to Cluster Sampling163

8.3 Mean of the Systematic Sample163

8.4 Variance of the Systematic Mean164

8.5 An Alternate Form for the Variance of ？sy164

8.6 Estimation of Sampling Variance166

8.7 Populations in Random Order169

8.8 Populations having Linear Trend170

8.9 Further Developments in Systematic Sampling171

8.10 Other Super Population Models173

PROBLEMS174

REFERENCES176

9 CLUSTER SAMPLING177

9.1 Necessity of Cluster Sampling177

9.2 Notation178

9.3 Precision of Survey Data179

9.4 Relation between Variance and Intracluster Correlation180

9.5 Estimation of M182

9.6 Cost Analysis182

9.7 Custer Sampling for Proportions185

9.8 Case of Unequal Cluster Sizes186

9.9 Probability Sampling Proportional to Size188

9.10 Comparison of the Three Methods192

PROBLEMS193

REFERENCES194

10 VARYING PROBABILITY SAMPLING:WITHOUT REPLACEMENT196

10.1 Introduction and Preliminaries196

10.2 Expected Values of Sums and Product-Sums199

10.3 Estimation of the Population Total200

10.4 Application of the Theory204

10.5 Systematic Sampling:Unequal Probabilities215

10.6 A New Systematic Sampling with an Unbiased Estimate of the Variance220

10.7 Computing Inclusion Probabilities and Estimation Procedures222

PROBLEMS227

REFERENCES228

11 TWO-PHASE AND REPETITIVE SAMPLING229

11.1 Introduction229

11.2 Difference Estimation229

11.3 Unbiased Ratio Estimation232

11.4 Biased Ratio Estimation232

11.5 Regression Estimation233

11.6 Estimation by Stratification237

11.7 Repetitive Surveys239

PROBLEMS242

REFERENCES245

12 TWO-STAGE SAMPLING246

12.1 Introduction246

12.2 Notation247

12.3 Estimation of Population Totals247

12.4 Two-Stage Scheme with Simple Random Sampling248

12.5 Comparison with Single-Stage and Custer Sampling252

12.6 Probability Sampling for a Two-Stage Design255

PROBLEMS259

REFERENCES262

13 NONSAMPLING ERRORS263

13.1 Introduction263

13.2 Effect of Nonresponse on Sample Mean and Proportion264

13.3 Required Sample Size When Nonresponse Is Present265

13.4 Conditional Inference When Nonresponse Exists269

13.5 Call-Backs269

13.6 A Probabilistic Model for Nonresponse276

13.7 Randomized Responses to Sensitive Questions280

13.8 Measurement Errors284

PROBLEMS286

REFERENCES288

14 BAYESIAN APPROACH FOR INFERENCE IN FINITE POPULATIONS289

14.1 Introduction289

14.2 Notation and the Model289

14.3 Some Basic Results291

14.4 Simple Random Sampling292

14.5 Hypergeometric-Binomial Model294

14.6 Stratified Sampling298

14.7 Two-Stage Sampling300

14.8 Response Error and Bias304

PROBLEMS306

REFERENCES308

15 THE JACKKNIFE METHOD309

15.1 Introduction309

15.2 The General Method309

15.3 Main Applications316

15.4 Interval Estimation317

15.5 Transformations317

15.6 The Bias in the Jackknife Estimate of the Variance318

PROBLEMS322

REFERENCES322

16 THE BOOTSTRAP METHOD324

16.1 Introduction324

16.2 The Bootstrap Method324

16.3 Bootstrap Methods for General Problems326

16.4 The Bootstrap Estimate of Bias327

16.5 Case of Finite Sample Space327

16.6 Regression Problems329

16.7 Bootstrap Confidence Intervals332

16.8 Application of Bootstrap Methods in Finance and Management Cases333

PROBLEMS333

REFERENCES334

17 SMALL-AREA ESTIMATION335

17.1 Introduction335

17.2 Demographic Methods336

17.3 Multiple Regression Methods338

17.4 Synthetic Estimators340

17.5 Composite Estimators341

PROBLEMS344

REFERENCES345

18 IMPUTATIONS IN SURVEYS347

18.1 Introduction347

18.2 General Rules for Imputing348

18.3 Methods of Imputation349

18.4 Evaluation of Imputation Procedures351

18.5 Secondary Data Analysis with Missing Observations353

18.6 A Procedure for Assessing the Quality of Inferences354

18.7 Bayesian Method356

18.8 Comparison of the Various Imputation Methods361

18.9 Multiple Imputation for Interval Estimation362

18.10 Normal-Based Analysis of a Multiple Imputed Data Set363

18.11 Confidence Interval for Population Mean Following Multiple Imputation366

PROBLEMS371

REFERENCES372

Answers to Selected Problems375

List of Cumulative References401

Author Index408

Subject Index411