Multivariate Global-Local Priors for Small Area Estimation
نویسندگان
چکیده
It is now widely recognized that small area estimation (SAE) needs to be model-based. Global-local (GL) shrinkage priors for random effects are important in sparse situations where many areas’ level do not have a significant impact on the response beyond what offered by covariates. We propose this paper hierarchical multivariate model with GL priors. prove propriety of posterior density when regression coefficient matrix has an improper uniform prior. Some concentration inequalities derived tail probabilities estimators. The proposed method illustrated via both data analysis and simulations.
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ژورنال
عنوان ژورنال: Stats
سال: 2022
ISSN: ['2571-905X']
DOI: https://doi.org/10.3390/stats5030040