Small Area Estimation
(Sprache: Englisch)
Small area estimation (SAE) has received a lot of attention in recent years due to growing demand for reliable small area statistics. Traditional area-specific direct estimates do not provide adequate precision for small areas because sample sizes in small areas are seldom large enough.
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Small area estimation (SAE) has received a lot of attention in recent years due to growing demand for reliable small area statistics. Traditional area-specific direct estimates do not provide adequate precision for small areas because sample sizes in small areas are seldom large enough.
Klappentext zu „Small Area Estimation “
An accessible introduction to indirect estimation methods, both traditional and model-based. Readers will also find the latest methods for measuring the variability of the estimates as well as the techniques for model validation.Uses a basic area-level linear model to illustrate the methodsPresents the various extensions including binary response data through generalized linear models and time series data through linear models that combine cross-sectional and time series features
Provides recent applications of SAE including several in U.S. Federal programs
Offers a comprehensive discussion of the design issues that impact SAE
Inhaltsverzeichnis zu „Small Area Estimation “
List of Figures.List of Tables.Foreword.Preface.1. Introduction.What is a Small Area?Demand for Small Area Statistics.Traditional Indirect Estimators.Small Area Models.Model-Based Estimation.Some Examples.2. Direct Domain Estimation .Introduction.Design-based Approach.Estimation of Totals.Domain Estimation.Modified Direct Estimators.Design Issues.Proofs.3. Traditional Demographic Methods .Introduction.Symptomatic Accounting Techniques.Regression Symptomatic Procedures.Dual-system Estimation of Total Population.Derivation of Average MSEs.4. Indirect Domain Estimation.Introduction .Synthetic Estimation.Composite Estimation.James-Stein Method.Proofs.5. Small Area Models .Introduction.Basic Area Level (Type A) Mode l.Basic Unit Level (Type B) Model.Extensions: Type A Models.Extensions: Type B Models.Generalized Linear Mixed Models.6. Empirical Best Linear Unbiased Prediction: Theory .Introduction.General Linear Mixed Model.Block Diagonal Covariance Structure.Proofs.7. EBLUP: Basic Models .Basic Area Level Model.Basic Unit Level Model.8. EBLUP: Extensions .Multivariate Fay-Herriot Model.Correlated Sampling Errors.Time Series and Cross-sectional Models.Spatial Models.Multivariate Nested Error Regression Model.Random Error Variances Linear Model.Two-fold Nested Error Regression Model.Two-level Model.9. Empirical Bayes (EB) Method .Introduction.Basic Area Level Model.Linear Mixed Models.Binary Data.Disease Mapping.Triple-goal Estimation.Empirical Linear Bayes.Constrained LB.Proofs.10. Hierarchical Bayes (HB) Method .Introduction.MCMC Methods.Basic Area Level Model.Unmatched Sampling and Linking Area Level Models.Basic Unit Level Model.General ANOVA Model.Two-level Models.Time Series and Cross-sectional Models.Multivariate Models.Disease Mapping Models.Binary Data.Exponential Family Models.Constrained HB.Proofs.References.Author Index.Subject Index.
Bibliographische Angaben
- Autor: J. N. K. Rao
- 2003, 344 Seiten, Maße: 24,2 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0471413747
- ISBN-13: 9780471413745
Sprache:
Englisch
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