A variable selection approach to multiple change-points detection with ordinal data

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 re-frame 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.