Bayesian grouped horseshoe regression with application to additive models

Zemei Xu, Daniel F. Schmidt, Enes Makalic, Guoqi Qian, John L. Hopper

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

The Bayesian horseshoe estimator is known for its robustness when handling noisy and sparse big data problems. This paper presents two extensions of the regular Bayesian horseshoe: (i) the grouped Bayesian horseshoe and (ii) the hierarchical Bayesian grouped horseshoe. The advantages of the proposed methods are their flexibility in handling grouped variables through extra shrinkage parameters at the group and within-group levels. We apply the proposed methods to the important class of additive models where group structures naturally exist, and we demonstrate that the grouped hierarchical Bayesian horseshoe has promising performance on both simulated and real data.

Original languageEnglish
Title of host publicationAI 2016: Advances in Artificial Intelligence
Subtitle of host publication29th Australasian Joint Conference Hobart, TAS, Australia, December 5–8, 2016 Proceedings
EditorsByeong Ho Kang, Quan Bai
Place of PublicationCham Switzerland
PublisherSpringer
Pages229-240
Number of pages12
ISBN (Electronic)9783319501277
ISBN (Print)9783319501260
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventAustralasian Joint Conference on Artificial Intelligence 2016 - Hobart, Australia
Duration: 5 Dec 20168 Dec 2016
Conference number: 29th
https://ai2016.net/
https://link.springer.com/book/10.1007/978-3-319-50127-7 (Proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9992
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceAustralasian Joint Conference on Artificial Intelligence 2016
Abbreviated titleAI 2016
CountryAustralia
CityHobart
Period5/12/168/12/16
Internet address

Keywords

  • Additive models
  • Bayesian regression
  • Grouped variables
  • Horseshoe

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