On Global-Local Shrinkage Priors for Count Data*

Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa

研究成果: Article査読

2 被引用数 (Scopus)

抄録

Global-local shrinkage priors have been recognized as a useful class of priors that can strongly shrink small signals toward prior means while keeping large signals unshrunk. Although such priors have been extensively discussed under Gaussian responses, in practice, we often encounter count responses. Previous contributions on global-local shrinkage priors cannot be readily applied to count data. In this paper, we discuss global-local shrinkage priors for analyzing a sequence of counts. We provide sufficient conditions under which the posterior mean is unshrunk for very large signals, known as the tail robustness property. Then, we propose tractable priors to satisfy those conditions approximately or exactly and develop a custom posterior computation algorithm for Bayesian inference without tuning parameters. We demonstrate the proposed methods through simulation studies and an application to a real dataset.

本文言語English
ページ(範囲)545-564
ページ数20
ジャーナルBayesian Analysis
17
2
DOI
出版ステータスPublished - 2022 6月
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 応用数学

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