序
前言
1 PREUMINARIES
1.1 Introduction
1.2 A Brief History of Survey Sampling
1.3 Sampling Designs and an Overview of Sampling
1.4 Ingredients of a Survey
1.5 Probability Sampling
1.6 Precision and Confidence Intervals
1.7 Biased Estimators
1.8 The Mean-Squared Error
1.9 Unbiased Estimation
PROBLEMS 8
REFERENCES 9
VARYING-PROBABILITY SAMPUNG
2.1 Introduction
2.2 Obtaining Varying-Probability Samples
2.3 Sampling Designs (Ordered and Unordered)
2.4 Sufficiency in Sampling from F'mite Populations
2.5 Sampling with Varying Probabilities and Without Replacement
PROBLEMS 26
REFERENCES 27
3 SIMPLE RANDOM SAMPLING
3.1 Introduction
3.2 Notation
3.3 Properties of Estimates
3.4 Variances of Estimators
3.5 Confidence Intervals
3.6 Alternate Method for Evaluating var (Y)
3.7 Random Sampling with Replacement
3.8 Estimates for Ratios
3.9 Estimates of Means or Totals over Subpopulations
3.10 Justification of the Normal Approximation
3.11 Asymptotic Normality of Estimates Arising from Simple Random Sampling
3.12 Best Unbiased Estimators
3.13 Distinct Units
3.14 The Distribution of W
3.15 Comparison of Simple Random Sampling with and without Replacement
3.16 Use of Balanced Incomplete Block Designs in Simple Random Sampling
3.17 Estimating Proportions and Percentages
3.18 Binomial and Hypergeometric Distributions and Their Use in Sampling
3.19 Confidence Limits for M
3.20 Confidence Intervals for Unknown Discrete Population Parameter
3.21 Use of the F'mite Population Correction for Binomial Confidence Limits
3.22 Cluster Sampling: Estimation of Proportions
PROBLEMS 59
REFERENCES 63
ESTIMATION OF THE SAMPLE SIZE
4.1 Introduction
4.2 Sample Size in Estimating Proportions
4.3 Inverse Sampling for Rare Attributes
4.4 Estimating Sample Size with Continuous Data
4.5 Estimation of S2
4.6 Estimation by Double Sampling
4.7 Estimation with Given Variance: Single Unknown Parameter
4.8 Sampling Procedure
4.9 Estimation of P with Specified Variance V
4.10 Estimation of P with Specified C V = C1/2
4.11 Estimation of y with Specified CV = C1/2
4.12 Estimation of y with Specified Variance V
4.13 Computing Sample Size: Decision-Theoretic Approach
PROBLEMS 73
REFERENCES 74
5 STRATIFIED SAMPUNG
5.1 Introduction
5.2 Estimators of Mean and Total and Their Properties
5.3 Confidence Limits (CI's)
5.4 Optimum Allocation of a Random Sample
5.5 Merits of Stratified Sampling (SS) Relative to Simple Random Sampling (SRS)
5.6 Modification of Optimal Allocation
5.7 Estimation of Sample Sizes in Stratified Sampling: Continuous Response Data
5.8 Estimation of the Population Mean y
5.9 Estimation of the Population Total
5.10 Application to Stratified Sampling for Proportions
5.11 Minimum Variance for Fixed n (Total Sample Size)
5.12 Gain by Stratified Sampling for Proportions
5.13 Sample Size for Proportions
5.14 Poststratification
5.15 How Should the Strata be Formed?2
5.16 Optimal Choice of L and n
5.17 Optimal Choice of L and n Via a Regression Variable
5.18 Controlled Sampling
5.19 Multiple Stratification
5.20 Interpenetrating Subsampling
PROBLEMS 108
REFERENCES 114
RATIO ESTIMATORS
6.1 Introduction
6.2 Variance of the Ratio Estimate
6.3 Estimates for var (YR)
6.4 Confidence Intervals for R
6.5 Efficiency Comparisons
6.6 An Optimum Property of the Ratio Estimators
6.7 Bias in the Ratio Estimate
6.8 An Exact Expression for the Bias of the Ratio Estimate
6.9 Ratio Estimates in Stratified Random Sampling
6.10 Comparison of YRs and YRc
6.11 Optimum Allocation with a Ratio Estimator
6.12 Unbiased Ratio Estimates
6.13 Jackknife Method for Obtaining a Ratio Estimate with Bias O(n-2)
6.14 Multivariate Ratio Estimators
6.15 A Dual Ratio Estimator
6.16 Comparison of Various Estimators
6.17 Unbiased Ratio Estimator
PROBLEMS 134
REFERENCES 142
7 REGRESSION ESTIMATORS
7.1 Introduction
7.2 Properties of Regression Estimators
7.3 Sample Estimate of Variance
7.4 Comparison of Regression, Ratio Estimates, and the Sample Mean
7.5 Properties of the Regression Estimator under a Super Population Model
7.6 Regression Estimates in Stratified Sampling
7.7 Sample Estimates
7.8 Unbiased Regression Estimation
PROBLEMS 156
REFERENCES 161
SYSTEMATIC SAMPUNG
8.1 Circular Systematic Sampling
8.2 Relation to Cluster Sampling
8.3 Mean of the Systematic Sample
8.4 Variance of the Systematic Mean
8.5 An Alternate Form for the Variance of Ysy
8.6 Estimation of Sampling Variance
8.7 Populations in Random Order
8.8 Populations having Linear Trend
8.9 Further Developments in Systematic Sampling
8.10 Other Super Population Models
PROBLEMS 174
REFERENCES 176
9 CLUSTER SAMPUNG
9.1 Necessity of Ouster Sampling
9.2 Notation
9.3 Precision of Survey Data
9.4 Relation between Variance and Intracluster Correlation
9.5 Estimation of M
9.6 Cost Analysis
9.7 Ouster Sampling for Proportions
9.8 Case of Unequal Cluster Sizes
9.9 Probability Sampling Proportional to Size
9.10 Comparison of the Three Methods
PROBLEMS 193
REFERENCES 194
lO VARYING PROBABILITY SAMPLING: WITHOUT REPLACEMENT
10.1 Introduction and Preliminaries
10.2 Expected Values of Sums and Product-Sums
10.3 Estimation of the Population Total
10.4 Application of the Theory
10.5 Systematic Sampling: Unequal Probabilities
10.6 A New Systematic Sampling with an Unbiased Estimate of the Variance
10.7 Computing Inclusion Probabilities and Estimation Procedures
PROBLEMS 227
REFERENCES 228
11 TWO-PHASE AND REPETITIVE SAMPUNG
11.1 Introduction
11.2 Difference Estimation
11.3 Unbiased Ratio Estimation
11.4 Biased Ratio Estimation
11.5 Regression Estimation
11.6 Estimation by Stratification
11.7 Repetitive Surveys
PROBLEMS 242
REFERENCES 245
12 TWO-STAGE SAMPLING
12.1 Introduction
12.2 Notation
12.3 Estimation of Population Totals
12.4 Two-Stage Scheme with Simple Random Sampling
12.5 Comparison with Single-Stage and Cluster Sampling
12.6 Probability Sampling for a Two-Stage Design
PROBLEMS 259
REFERENCES 262
13 NONSAMPUNG ERRORS
13.1 Introduction
13.2 Effect of Nonresponse on Sample Mean and Proportion
13.3 Required Sample Size When Nonresponse Is Present
13.4 Conditional Inference When Nonresponse Exists
13.5 Call-Backs
13.6 A Probabilistic Model for Nonresponse
13.7 Randomized Responses to Sensitive Questions
13.8 Measurement Errors
PROBLEMS 286
REFERENCES 288
14 BAYESIAN APPROACH FOR INFERENCE IN FINITE POPULATIONS
14.1 Introduction
14.2 Notation and the Model
14.3 Some Basic Results
14.4 Simple Random Sampling
14.5 Hypergeometric-Binomial Model
14.6 Stratified Sampling
14.7 Two-Stage Sampling
14.8 Response Error and Bias
PROBLEMS 306
REFERENCES 3O8
15 THE JACKKNIFE METHOD
15.1 Introduction
15.2 The General Method
15.3 Main Applications
15.4 Interval Estimation
15.5 Transformations
15.6 The Bias in the Jackknife Estimate of the Variance
PROBLEMS 322
REFERENCES 322
16 THE BOOTSTRAP METHOD
16.1 Introduction
16.2 The Bootstrap Method
16.3 Bootstrap Methods for General Problems
16.4 The Bootstrap Estimate of Bias
16.5 Case of Firnite Sample Space
16.6 Regression Problems
16.7 Bootstrap Confidence Intervals
16.8 Application of Bootstrap Methods in Finance and Management Cases
PROBLEMS 333
REFERENCES 334
17 SMALL-AREA ESTIMATION
17.1 Introduction
17.2 Demographic Methods
17.3 Multiple Regression Methods
17.4 Synthetic Estimators
17.5 Composite Estimators
PROBLEMS 344
REFERENCES 345
18 IMPUTATIONS IN SURVEYS
18.1 Introduction
18.2 General Rules for Imputing
18.3 Methods of Imputation
18.4 Evaluation of Imputation Procedures
18.5 Secondary Data Analysis with Missing Observations
18.6 A Procedure for Assessing the Quality of Inferences
18.7 Bayesian Method
18.8 Comparison of the Various Imputation Methods
18.9 Multiple Imputation for Interval Estimation
18.10 Normal-Based Analysis of a Multiple Imputed Data Set
18.11 Confidence Interval for Population Mean Following Multiple Imputation
PROBLEMS 371
REFERENCES 372
Answers to Selected Problems
List of Cumulative References
Author Index
Subject Index