Small Area Estimation

Praise for the First Edition This pioneering work, in which Rao provides a comprehensive and up–to–date treatment of small area estimation, will become a classic...I believe that it has the potential to turn small area estimation...into a larger area of importance to both researchers and practitioners. – Journal of the American Statistical Association Written by two experts in the field, Small Area Estimation, Second Edition provides a comprehensive and up–to–date account of the methods and theory of small area estimation (SAE), particularly indirect estimation based on explicit small area linking models. The model–based approach to small area estimation offers several advantages including increased precision, the derivation of optimal estimates and associated measures of variability under an assumed model, and the validation of models from the sample data. Emphasizing real data throughout, the Second Edition maintains a self–contained account of crucial theoretical and methodological developments in the field of SAE. The new edition provides extensive accounts of new and updated research, which often involves complex theory to handle model misspecifications and other complexities. In addition to the information on survey design issues and traditional methods employing indirect estimates based on implicit linking models, Small Area Estimation, Second Edition also features: Additional sections describe an R package for SAE and applications with R data sets that readers can replicate Numerous examples of SAE applications throughout the book, including recent applications in U.S. Federal programs New topical coverage on extended design issues, synthetic estimation, further refinements and solutions to the Fay–Herriot area level model, basic unit level models, and spatial and time series models A discussion of the advantages and limitations of various SAE methods for model selection from data as well as comparisons of estimates derived from models to reliable values obtained from external sources, such as previous census or administrative data Small Area Estimation, Second Edition is an excellent reference for practicing statisticians and survey methodologists as well as practitioners interested in learning SAE methods. The Second Edition is also an ideal textbook for graduate–level courses in SAE and reliable small area statistics.   INDICE: List of Figures viiiList of Tables xForeword xiiPreface xv1 Introduction 11.1 What is a Small Area? 11.2 Demand for Small Area Statistics 31.3 Traditional Indirect Estimators 41.4 Small Area Models 41.5 Model–Based Estimation 51.6 Some Examples 62 Direct Domain Estimation 92.1 Introduction 92.2 Design–based Approach 102.3 Estimation of Totals 112.4 Domain Estimation 162.5 Modi—ed GREG Estimator 212.6 Design Issues 232.7 Optimal sample allocation for planned domains 262.8 Proofs 323 Indirect Domain Estimation 353.1 Introduction 353.2 Synthetic Estimation 353.3 Composite Estimation 583.4 James–Stein Method 643.5 Proofs 734 Small Area Models 774.1 Introduction 774.2 Basic Area Level Model 784.3 Basic Unit Level Model 804.4 Extensions: Area Level Models 834.5 Extensions: Unit Level Models 904.6 Generalized Linear Mixed Models 945 Empirical Best Linear Unbiased Prediction: Theory 995.1 Introduction 995.2 General Linear Mixed Model 1005.3 Block Diagonal Covariance Structure 1105.4 Model Identi—cation and Checking 1135.5 Software 1205.6 Proofs 1216 EBLUP: Basic Area Level Model 1256.1 EBLUP estimation 1256.2 MSE Estimation 1386.3 Robust estimation in the presence of outliers 1486.4 Practical issues 1506.5 Software 1727 Basic Unit Level Model 1757.1 EBLUP estimation 1757.2 MSE Estimation 1817.3 Applications 1887.4 Outlier robust EBLUP estimation 1957.5 M–quantile regression 2027.6 Practical Issues 2077.7 Software 2297.8 Proofs 2338 EBLUP: Extensions 2358.1 Multivariate Fay–Herriot Model 2358.2 Correlated Sampling Errors 2378.3 Time Series and Cross–sectional Models 2408.4 Spatial Models 2498.5 Two–fold Subarea Level Models 2528.6 Multivariate Nested Error Regression Model 2538.7 Two–fold Nested Error Regression Model 2558.8 Two–level Model 2598.9 Models for Multinomial Counts 2618.10 EBLUP for Vectors of Area Proportions 2638.11 Software 2649 Empirical Bayes (EB) Method 2699.1 Introduction 2699.2 Basic Area Level Model 2709.3 Linear Mixed Models 2879.4 EB estimation of general —nite population parameters 2899.5 Binary Data 2989.6 Disease Mapping 3099.7 Design–weighted EB estimation: exponential family models 3149.8 Triple–goal Estimation 3179.9 Empirical Linear Bayes 3209.10 Constrained LB 3259.11 Software 3269.12 Proofs 33110 Hierarchical Bayes (HB) Method 33510.1 Introduction 33510.2 MCMC Methods 33610.3 Basic Area Level Model 34910.4 Unmatched Sampling and Linking Area Level Models 35910.5 Basic Unit Level Model 36410.6 General ANOVA Model 37110.7 HB estimation of general —nite population parameters 37210.8 Two–level Models 37710.9 Time Series and Cross–sectional Models 38010.10 Multivariate Models 38510.11 Disease Mapping Models 38610.12 Two–part Nested Error Model 39110.13 Binary Data 39210.14 Missing Binary Data 40110.15 Natural Exponential Family Models 40210.16 Constrained HB 40310.17 Approximate HB Inference and Data Cloning 40410.18 Proofs 405References 409

• ISBN: 978-1-118-73578-7
• Editorial: Wiley–Blackwell
• Encuadernacion: Rústica
• Páginas: 480
• Fecha Publicación: 25/09/2015
• Nº Volúmenes: 1
• Idioma: Inglés