Statistics and Its Interface
Volume 13 (2020)
A Variable Selection Approach to Multiple Change-Points Detection
Pages: 251 – 260
Change-point detection has been studied extensively with continuous data, while much less research has been carried out for categorical data. Focusing on ordinal data, we reframe the change-point detection problem in a Bayesian variable selection context. We propose a latent probit model in conjunction with reversible jump Markov chain Monte Carlo to estimate both the number and locations of change-points with ordinal data. We conduct extensive simulation studies to assess the performance of our method. As an illustration, we apply the new method to detect changes in the ordinal data from the north Atlantic tropical cyclone record, which has an indication of global warming in the past decades.
Multinomial data, Multiple change-points, Ordinal data, Reversible-jump Markov chain Monte Carlo
The authors’ research was supported in part by a grant (no. 17307218) from the Research Grants Council of Hong Kong.
Received 14 May 2019
Accepted 30 November 2019
Published 30 January 2020