Sample Surveys: Inference and Analysis (eBook)

Front Cover 1
Title Page 4
Copyright Page 5
Preface to Handbook 29B 6
Table of Contents 8
Contributors: Vol. 29B 20
Part 4: Alternative Approaches to Inference from Survey Data 26
Introduction to Part 4 28
1. Introduction 28
2. Modes of inference with survey data 29
3. Overview of Part 4 33
Chapter 23. Model-Based Prediction of Finite Population Totals 36
1. Superpopulation models and some simple examples 36
2. Prediction under the general linear model 39
3. Estimation weights 42
4. Weighted balance and robustness 43
5. Variance estimation 45
6. Models with qualitative auxiliaries 47
7. Clustered populations 48
8. Estimation under nonlinear models 54
Chapter 24. Design- and Model-Based Inference for Model Parameters 58
1. Introduction and scope 58
2. Survey populations and target populations 59
3. Statistical inferences 63
4. General theory for fitting models 66
5. Cases where design-based methods can be problematic 72
6. Estimation of design-based variances 76
7. Integrating data from more than one survey 77
8. Some final remarks 78
Chapter 25. Calibration Weighting: Combining Probability Samples and Linear Prediction Models 80
1. Introduction 80
2. Randomization consistency and other asymptotic properties 83
3. The GREG estimator 85
4. Redefining calibration weights 90
5. Variance estimation 94
6. Nonlinear calibration 98
7. Calibration and quasi-randomization 101
8. Other approaches, other issues 105
Acknowledgements 107
Chapter 26. Estimating Functions and Survey Sampling 108
1. Introduction 108
2. Defining finite population and superpopulation parameters through estimating functions 109
3. Design-unbiased estimating functions 110
4. Optimality 112
5. Asymptotic properties of sample estimating functions and their roots 114
6. Interval estimation from estimating functions 117
7. Bootstrapping estimating functions 121
8. Multivariate and nuisance parameters 122
9. Estimating functions and imputation 124
Acknowledgment 126
Chapter 27. Nonparametric and Semiparametric Estimation in Complex Surveys 128
1. Introduction 128
2. Nonparametric methods in descriptive inference from surveys 132
3. Nonparametric methods in analytic inference from surveys 139
4. Nonparametric methods in nonresponse adjustment 140
5. Nonparametric methods in small area estimation 143
Chapter 28. Resampling Methods in Surveys 146
1. Introduction 146
2. The basic notions of bootstrap and jackknife 148
3. Methods for more complex survey designs and estimators 151
4. Variance estimation in the presence of imputation 159
5. Resampling methods for sampling designs in two phases 162
6. Resampling methods in the prediction approach 163
7. Resampling methods in small area estimation 165
8. Discussion 174
Acknowledgments 176
Chapter 29. Bayesian Developments in Survey Sampling 178
1. Introduction 178
2. Notation and preliminaries 179
3. The Bayesian paradigm 181
4. Linear Bayes estimator 186
5. Bayes estimators of the finite population mean under more complex models 189
6. Stratified sampling and domain estimation 199
7. Generalized linear models 204
8. Summary 211
Acknowledgments 212
Chapter 30. Empirical Likelihood Methods 214
1. Likelihood-based approaches 214
2. Empirical likelihood method under simple random sampling 216
3. Stratified simple random sampling 218
4. Pseudo empirical likelihood method 219
5. Computational algorithms 230
6. Discussion 231
Acknowledgments 232
Part 5: Special Estimation and Inference Problems 234
Introduction to Part 5 236
1. Preface 236
2. Overview of chapters in Part 5 237
Chapter 31. Design-based Methods of Estimation for Domains and Small Areas 244
1. Introduction 244
2. Theoretical framework, terminology, and notation 246
3. Direct estimators for domain estimation 251
4. Indirect estimators in domain estimation 258
5. Extended GREG family for domain estimation 269
6. Software 273
Acknowledgments 274
Chapter 32. Model-Based Approach to Small Area Estimation 276
1. Introduction 276
2. Model-based frequentist small area estimation 278
3. Bayesian approach to small area estimation 295
4. Concluding remarks 309
Acknowledgements 313
Chapter 33. Design and Analysis of Surveys Repeated over Time 314
1. Overview of issues for repeated surveys 314
2. Basic theory of design and estimation for repeated surveys 318
3. Rotation patterns 321
4. Best linear and composite estimation 322
5. Correlation models for sampling errors 326
6. Rotation patterns and sampling variances 329
7. Time series methods for estimation in repeated surveys 330
8. Seasonal adjustment and trend estimation 334
9. Variance estimation for seasonally adjusted and trend estimates 336
10. Rotation patterns and seasonally adjusted and trend estimates 338
Chapter 34. The Analysis of Longitudinal Surveys 340
1. Introduction 340
2. Types and problems of longitudinal surveys 341
3. General models for analysis of longitudinal data 343
4. Treatment of nonresponse 347
5. Effects of informative sample design on longitudinal analysis 350
Chapter 35. Categorical Data Analysis for Simple and Complex Surveys 354
1. Introduction 354
2. Likelihood-based methods 357
3. Quasi-likelihood methods 370
4. Weighted quasi-likelihood methods 375
5. Unit-level models 386
6. Conclusions 392
Acknowledgments 394
Chapter 36. Inference on Distribution Functions and Quantiles 396
1. Introduction 396
2. Estimating the distribution function with no auxiliary information 399
3. Estimating the distribution function with complete auxiliary information 401
4. Estimating the distribution function using partial auxiliary information 414
5. Quantile estimation 415
6. Variance estimation and confidence intervals for distribution functions 417
7. Confidence intervals and variance estimates for quantiles 418
8. Further results and questions 420
Chapter 37. Scatterplots with Survey Data 422
1. Introduction 422
2. Modifications of scatterplots for survey data 422
3. Discussion 444
Part 6: Informative Sampling and Theoretical Aspects 446
Introduction to Part 6 448
1. Motivation 448
2. Overview of chapters in Part 6 451
Chapter 38. Population-Based Case–Control Studies 456
1. Introduction to case–control sampling 456
2. Basic results 459
3. Two-phase case–control sampling 472
4. Case–control family studies 476
5. Conclusion 478
Chapter 39. Inference under Informative Sampling 480
1. Introduction 480
2. Informative and ignorable sampling 484
3. Overview of approaches that account for informative sampling and nonresponse 486
4. Use of the sample distribution for inference 493
5. Prediction under informative sampling 500
6. Other applications of the sample distribution 504
7. Tests of sampling ignorability 509
8. Brief summary 511
Acknowledgements 512
Chapter 40. Asymptotics in Finite Population Sampling 514
1. Introduction 514
2. Asymptotics in SRS 515
3. Resampling in FPS: Asymptotics 520
4. Estimation of population size: Asymptotics 524
5. Sampling with varying probabilities: Asymptotics 527
6. Large entropy and relative samplings: Asymptotic results 535
7. Successive subsampling with varying probabilities: Asymptotics 543
8. Conclusions 546
Acknowledgment 547
Chapter 41. Some Decision-Theoretic Aspects of Finite Population Sampling 548
1. Introduction 548
2. Notations and definitions 549
3. Minimax strategies 553
4. UMVU estimators 566
5. Admissibility 567
6. Superpopulation models 571
7. Beyond simple random sampling 577
8. List of main notations 582
Acknowledgements 583
References 584
Subject Index: Index of Vol. 29B 620
Handbook of Statistics: Contents of Previous Volumes 640