Improved Small Area Inference from Data Integration Using Global-Local Priors

Fuente: arXiv
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Autores principales: Cahoy, D, Sedransk, J
Formato: Preprint
Publicado: 2024
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author Cahoy, D
Sedransk, J
author_facet Cahoy, D
Sedransk, J
contents We present and apply methodology to improve inference for small area parameters by using data from several sources. This work extends Cahoy and Sedransk (2023) who showed how to integrate summary statistics from several sources. Our methodology uses hierarchical global-local prior distributions to make inferences for the proportion of individuals in Florida's counties who do not have health insurance. Results from an extensive simulation study show that this methodology will provide improved inference by using several data sources. Among the five model variants evaluated the ones using horseshoe priors for all variances have better performance than the ones using lasso priors for the local variances.
format Preprint
id arxiv_https___arxiv_org_abs_2412_07824
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Improved Small Area Inference from Data Integration Using Global-Local Priors
Cahoy, D
Sedransk, J
Methodology
We present and apply methodology to improve inference for small area parameters by using data from several sources. This work extends Cahoy and Sedransk (2023) who showed how to integrate summary statistics from several sources. Our methodology uses hierarchical global-local prior distributions to make inferences for the proportion of individuals in Florida's counties who do not have health insurance. Results from an extensive simulation study show that this methodology will provide improved inference by using several data sources. Among the five model variants evaluated the ones using horseshoe priors for all variances have better performance than the ones using lasso priors for the local variances.
title Improved Small Area Inference from Data Integration Using Global-Local Priors
topic Methodology
url https://arxiv.org/abs/2412.07824