Robust Bayesian Changepoint Analysis in the Presence of Outliers

Shonosuke Sugasawa, Shintaro Hashimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We introduce a new robust Bayesian change-point analysis in the presence of outliers. We employ an idea of general posterior based on density power divergence combined with horseshoe prior for differences of underlying signals. A posterior computation algorithm is proposed using Markov chain Monte Carlo. The proposed method is demonstrated through simulation and real data analysis.

Original languageEnglish
Title of host publicationIntelligent Decision Technologies - Proceedings of the 13th KES-IDT 2021 Conference
EditorsIreneusz Czarnowski, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer Science and Business Media Deutschland GmbH
Pages469-478
Number of pages10
ISBN (Print)9789811627644
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event13th International KES Conference on Intelligent Decision Technologies, KES-IDT 2021 - Virtual, Online
Duration: 2021 Jun 142021 Jun 16

Publication series

NameSmart Innovation, Systems and Technologies
Volume238
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference13th International KES Conference on Intelligent Decision Technologies, KES-IDT 2021
CityVirtual, Online
Period21/6/1421/6/16

Keywords

  • Change-point analysis
  • Density power divergence
  • Horseshoe prior
  • State space model

ASJC Scopus subject areas

  • General Decision Sciences
  • General Computer Science

Fingerprint

Dive into the research topics of 'Robust Bayesian Changepoint Analysis in the Presence of Outliers'. Together they form a unique fingerprint.

Cite this