Read J. N. K. Rao - Wiley Series in Survey Methodology: Small Area Estimation in DJV
9781118735725 English 1118735722 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., Written by an accomplished team of 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, 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. Emphasis is on real data throughout the book. More than two dozen new topics have been added to this edition (such as 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) along with selected R subroutines.
9781118735725 English 1118735722 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., Written by an accomplished team of 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, 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. Emphasis is on real data throughout the book. More than two dozen new topics have been added to this edition (such as 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) along with selected R subroutines.