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Small Area Estimation
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Small Area Estimation

Small Area Estimation

J.N.K. Rao

336 pages, parution le 10/02/2003

Résumé

A much-needed guide to reliable small area statistics

The term "small area" denotes any subpopulation for which direct estimates of adequate precision cannot be produced. In recent years, the demand for reliable small area estimates has greatly increased worldwide due to, among other things, their growing use in formulating policies and programs and the allocation of government funds; regional planning; small business decisions; and similar applications.

Small Area Estimation provides a comprehensive 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.

The clear, detailed coverage includes:
  • Basic terminology related to small area estimation
  • Survey design issues and traditional methods employing indirect estimates based on implicit linking models
  • Linear mixed models and generalized linear mixed models
  • Empirical Best Linear Unbiased Prediction (EBLUP), Empirical Bayes (EB) and Hierarchical Bayes (HB) Estimation
  • Model diagnostics
  • Various extensions including binary response and count data through generalized linear mixed models and time series data through linear mixed models that combine cross-sectional and time series features
  • Important applications of SAE including several in U.S. federal programs

Contents

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) Model
  • 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

Caractéristiques techniques

  PAPIER
Éditeur(s) Wiley
Auteur(s) J.N.K. Rao
Parution 10/02/2003
Nb. de pages 336
Format 16 x 24
Couverture Relié
Poids 559g
Intérieur Noir et Blanc
EAN13 9780471413745
ISBN13 978-0-471-41374-5

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