Pfeffermann / Rao Sample Surveys: Inference and Analysis

1. Auflage 2009
ISBN: 978-0-08-096354-9
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 666 Seiten

Reihe: ISSN

This new handbook contains the most comprehensive account of sample surveys theory and practice to date. It is a second volume on sample surveys, with the goal of updating and extending the sampling volume published as volume 6 of the Handbook of Statistics in 1988. The present handbook is divided into two volumes (29A and 29B), with a total of 41 chapters, covering current developments in almost every aspect of sample surveys, with references to important contributions and available software. It can serve as a self contained guide to researchers and practitioners, with appropriate balance between theory and real life applications. Each of the two volumes is divided into three parts, with each part preceded by an introduction, summarizing the main developments in the areas covered in that part. Volume 1 deals with methods of sample selection and data processing, with the later including editing and imputation, handling of outliers and measurement errors, and methods of disclosure control. The volume contains also a large variety of applications in specialized areas such as household and business surveys, marketing research, opinion polls and censuses. Volume 2 is concerned with inference, distinguishing between design-based and model-based methods and focusing on specific problems such as small area estimation, analysis of longitudinal data, categorical data analysis and inference on distribution functions. The volume contains also chapters dealing with case-control studies, asymptotic properties of estimators and decision theoretic aspects.
Comprehensive account of recent developments in sample survey theory and practice Covers a wide variety of diverse applications Comprehensive bibliography

Autoren/Hrsg.

Pfeffermann, Danny

Rao, C.R.

Inhaltsverzeichnis

1;Front Cover;1
2;Title Page;4
3;Copyright Page;5
4;Preface to Handbook 29B;6
5;Table of Contents;8
6;Contributors: Vol. 29B;20
7;Part 4: Alternative Approaches to Inference from Survey Data;26
7.1;Introduction to Part 4;28
7.1.1;1. Introduction;28
7.1.2;2. Modes of inference with survey data;29
7.1.3;3. Overview of Part 4;33
7.2;Chapter 23. Model-Based Prediction of Finite Population Totals;36
7.2.1;1. Superpopulation models and some simple examples;36
7.2.2;2. Prediction under the general linear model;39
7.2.3;3. Estimation weights;42
7.2.4;4. Weighted balance and robustness;43
7.2.5;5. Variance estimation;45
7.2.6;6. Models with qualitative auxiliaries;47
7.2.7;7. Clustered populations;48
7.2.8;8. Estimation under nonlinear models;54
7.3;Chapter 24. Design- and Model-Based Inference for Model Parameters;58
7.3.1;1. Introduction and scope;58
7.3.2;2. Survey populations and target populations;59
7.3.3;3. Statistical inferences;63
7.3.4;4. General theory for fitting models;66
7.3.5;5. Cases where design-based methods can be problematic;72
7.3.6;6. Estimation of design-based variances;76
7.3.7;7. Integrating data from more than one survey;77
7.3.8;8. Some final remarks;78
7.4;Chapter 25. Calibration Weighting: Combining Probability Samples and Linear Prediction Models;80
7.4.1;1. Introduction;80
7.4.2;2. Randomization consistency and other asymptotic properties;83
7.4.3;3. The GREG estimator;85
7.4.4;4. Redefining calibration weights;90
7.4.5;5. Variance estimation;94
7.4.6;6. Nonlinear calibration;98
7.4.7;7. Calibration and quasi-randomization;101
7.4.8;8. Other approaches, other issues;105
7.4.9;Acknowledgements;107
7.5;Chapter 26. Estimating Functions and Survey Sampling;108
7.5.1;1. Introduction;108
7.5.2;2. Defining finite population and superpopulation parameters through estimating functions;109
7.5.3;3. Design-unbiased estimating functions;110
7.5.4;4. Optimality;112
7.5.5;5. Asymptotic properties of sample estimating functions and their roots;114
7.5.6;6. Interval estimation from estimating functions;117
7.5.7;7. Bootstrapping estimating functions;121
7.5.8;8. Multivariate and nuisance parameters;122
7.5.9;9. Estimating functions and imputation;124
7.5.10;Acknowledgment;126
7.6;Chapter 27. Nonparametric and Semiparametric Estimation in Complex Surveys;128
7.6.1;1. Introduction;128
7.6.2;2. Nonparametric methods in descriptive inference from surveys;132
7.6.3;3. Nonparametric methods in analytic inference from surveys;139
7.6.4;4. Nonparametric methods in nonresponse adjustment;140
7.6.5;5. Nonparametric methods in small area estimation;143
7.7;Chapter 28. Resampling Methods in Surveys;146
7.7.1;1. Introduction;146
7.7.2;2. The basic notions of bootstrap and jackknife;148
7.7.3;3. Methods for more complex survey designs and estimators;151
7.7.4;4. Variance estimation in the presence of imputation;159
7.7.5;5. Resampling methods for sampling designs in two phases;162
7.7.6;6. Resampling methods in the prediction approach;163
7.7.7;7. Resampling methods in small area estimation;165
7.7.8;8. Discussion;174
7.7.9;Acknowledgments;176
7.8;Chapter 29. Bayesian Developments in Survey Sampling;178
7.8.1;1. Introduction;178
7.8.2;2. Notation and preliminaries;179
7.8.3;3. The Bayesian paradigm;181
7.8.4;4. Linear Bayes estimator;186
7.8.5;5. Bayes estimators of the finite population mean under more complex models;189
7.8.6;6. Stratified sampling and domain estimation;199
7.8.7;7. Generalized linear models;204
7.8.8;8. Summary;211
7.8.9;Acknowledgments;212
7.9;Chapter 30. Empirical Likelihood Methods;214
7.9.1;1. Likelihood-based approaches;214
7.9.2;2. Empirical likelihood method under simple random sampling;216
7.9.3;3. Stratified simple random sampling;218
7.9.4;4. Pseudo empirical likelihood method;219
7.9.5;5. Computational algorithms;230
7.9.6;6. Discussion;231
7.9.7;Acknowledgments;232
8;Part 5: Special Estimation and Inference Problems;234
8.1;Introduction to Part 5;236
8.1.1;1. Preface;236
8.1.2;2. Overview of chapters in Part 5;237
8.2;Chapter 31. Design-based Methods of Estimation for Domains and Small Areas;244
8.2.1;1. Introduction;244
8.2.2;2. Theoretical framework, terminology, and notation;246
8.2.3;3. Direct estimators for domain estimation;251
8.2.4;4. Indirect estimators in domain estimation;258
8.2.5;5. Extended GREG family for domain estimation;269
8.2.6;6. Software;273
8.2.7;Acknowledgments;274
8.3;Chapter 32. Model-Based Approach to Small Area Estimation;276
8.3.1;1. Introduction;276
8.3.2;2. Model-based frequentist small area estimation;278
8.3.3;3. Bayesian approach to small area estimation;295
8.3.4;4. Concluding remarks;309
8.3.5;Acknowledgements;313
8.4;Chapter 33. Design and Analysis of Surveys Repeated over Time;314
8.4.1;1. Overview of issues for repeated surveys;314
8.4.2;2. Basic theory of design and estimation for repeated surveys;318
8.4.3;3. Rotation patterns;321
8.4.4;4. Best linear and composite estimation;322
8.4.5;5. Correlation models for sampling errors;326
8.4.6;6. Rotation patterns and sampling variances;329
8.4.7;7. Time series methods for estimation in repeated surveys;330
8.4.8;8. Seasonal adjustment and trend estimation;334
8.4.9;9. Variance estimation for seasonally adjusted and trend estimates;336
8.4.10;10. Rotation patterns and seasonally adjusted and trend estimates;338
8.5;Chapter 34. The Analysis of Longitudinal Surveys;340
8.5.1;1. Introduction;340
8.5.2;2. Types and problems of longitudinal surveys;341
8.5.3;3. General models for analysis of longitudinal data;343
8.5.4;4. Treatment of nonresponse;347
8.5.5;5. Effects of informative sample design on longitudinal analysis;350
8.6;Chapter 35. Categorical Data Analysis for Simple and Complex Surveys;354
8.6.1;1. Introduction;354
8.6.2;2. Likelihood-based methods;357
8.6.3;3. Quasi-likelihood methods;370
8.6.4;4. Weighted quasi-likelihood methods;375
8.6.5;5. Unit-level models;386
8.6.6;6. Conclusions;392
8.6.7;Acknowledgments;394
8.7;Chapter 36. Inference on Distribution Functions and Quantiles;396
8.7.1;1. Introduction;396
8.7.2;2. Estimating the distribution function with no auxiliary information;399
8.7.3;3. Estimating the distribution function with complete auxiliary information;401
8.7.4;4. Estimating the distribution function using partial auxiliary information;414
8.7.5;5. Quantile estimation;415
8.7.6;6. Variance estimation and confidence intervals for distribution functions;417
8.7.7;7. Confidence intervals and variance estimates for quantiles;418
8.7.8;8. Further results and questions;420
8.8;Chapter 37. Scatterplots with Survey Data;422
8.8.1;1. Introduction;422
8.8.2;2. Modifications of scatterplots for survey data;422
8.8.3;3. Discussion;444
9;Part 6: Informative Sampling and Theoretical Aspects;446
9.1;Introduction to Part 6;448
9.1.1;1. Motivation;448
9.1.2;2. Overview of chapters in Part 6;451
9.2;Chapter 38. Population-Based Case–Control Studies;456
9.2.1;1. Introduction to case–control sampling;456
9.2.2;2. Basic results;459
9.2.3;3. Two-phase case–control sampling;472
9.2.4;4. Case–control family studies;476
9.2.5;5. Conclusion;478
9.3;Chapter 39. Inference under Informative Sampling;480
9.3.1;1. Introduction;480
9.3.2;2. Informative and ignorable sampling;484
9.3.3;3. Overview of approaches that account for informative sampling and nonresponse;486
9.3.4;4. Use of the sample distribution for inference;493
9.3.5;5. Prediction under informative sampling;500
9.3.6;6. Other applications of the sample distribution;504
9.3.7;7. Tests of sampling ignorability;509
9.3.8;8. Brief summary;511
9.3.9;Acknowledgements;512
9.4;Chapter 40. Asymptotics in Finite Population Sampling;514
9.4.1;1. Introduction;514
9.4.2;2. Asymptotics in SRS;515
9.4.3;3. Resampling in FPS: Asymptotics;520
9.4.4;4. Estimation of population size: Asymptotics;524
9.4.5;5. Sampling with varying probabilities: Asymptotics;527
9.4.6;6. Large entropy and relative samplings: Asymptotic results;535
9.4.7;7. Successive subsampling with varying probabilities: Asymptotics;543
9.4.8;8. Conclusions;546
9.4.9;Acknowledgment;547
9.5;Chapter 41. Some Decision-Theoretic Aspects of Finite Population Sampling;548
9.5.1;1. Introduction;548
9.5.2;2. Notations and definitions;549
9.5.3;3. Minimax strategies;553
9.5.4;4. UMVU estimators;566
9.5.5;5. Admissibility;567
9.5.6;6. Superpopulation models;571
9.5.7;7. Beyond simple random sampling;577
9.5.8;8. List of main notations;582
9.5.9;Acknowledgements;583
9.6;References;584
9.7;Subject Index: Index of Vol. 29B;620
9.8;Handbook of Statistics: Contents of Previous Volumes;640