Bayesian Estimators for Small Area Models Shrinking Both Means and Variances

DOI10.1111/SJOS.12246zbMath1361.62008arXiv1507.05179OpenAlexW2340792812MaRDI QIDQ2965540

Hiromasa Tamae, Shonosuke Sugasawa, Tatsuya Kubokawa

Publication date: 3 March 2017

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1507.05179

zbMATH Keywords

mean squared errorMarkov chain Monte CarloBayesian estimationGibbs samplingposterior proprietysmall area estimationFay-Herriot modelshrinking both means and variances

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Sampling theory, sample surveys (62D05) Empirical decision procedures; empirical Bayes procedures (62C12)

Related Items (10)

Small area estimation via unmatched sampling and linking models ⋮ Small area estimation with mixed models: a review ⋮ Benchmarked linear shrinkage prediction in the Fay–Herriot small area model ⋮ Robust prediction of domain compositions from uncertain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model ⋮ A new robust Bayesian small area estimation via ‐stable model for estimating the proportion of athletic students in California ⋮ Bivariate hierarchical Bayesian model for combining summary measures and their uncertainties from multiple sources ⋮ A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates ⋮ Joint point and variance estimation under a hierarchical Bayesian model for survey count data ⋮ Improved Small Domain Estimation via Compromise Regression Weights ⋮ Multilevel time-series models for small area estimation at different frequencies and domain levels

Cites Work

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• Small area estimation: an appraisal. With comments and a rejoinder by the authors
• Prediction Error of Small Area Predictors Shrinking Both Means and Variances
• Small Area Estimation
• Empirical Bayes Confidence Intervals Shrinking Both Means and Variances
• The Mean Squared Error of Small Area Predictors Constructed With Estimated Area Variances
• Bayesian Measures of Model Complexity and Fit
• Small Area Estimation-New Developments and Directions
• Bayesian Estimators for Small Area Models when Auxiliary Information is Measured with Error

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