Bayesian fused lasso modeling via horseshoe prior

Yuko Kakikawa, Kaito Shimamura, Shuichi Kawano

研究成果: ジャーナルへの寄稿学術誌査読

3 被引用数 (Scopus)

抄録

Bayesian fused lasso is one of the sparse Bayesian methods, which shrinks both regression coefficients and their successive differences simultaneously. In this paper, we propose a Bayesian fused lasso modeling via horseshoe prior. By assuming a horseshoe prior on the difference of successive regression coefficients, the proposed method enables us to prevent over-shrinkage of those differences. We also propose a Bayesian nearly hexagonal operator for regression with shrinkage and equality selection with horseshoe prior, which imposes priors on all combinations of differences of regression coefficients. Simulation studies and an application to real data show that the proposed method gives better performance than existing methods.

本文言語英語
ページ(範囲)705-727
ページ数23
ジャーナルJapanese Journal of Statistics and Data Science
6
2
DOI
出版ステータス出版済み - 11月 2023

!!!All Science Journal Classification (ASJC) codes

  • 統計学および確率
  • 計算理論と計算数学

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