Bayesian Analysis

On a Nonparametric Change Point Detection Model in Markovian Regimes

Asael Fabian Martínez and Ramsés H. Mena

Full-text: Open access

Abstract

Change point detection models aim to determine the most probable grouping for a given sample indexed on an ordered set. For this purpose, we propose a methodology based on exchangeable partition probability functions, specifically on Pitman’s sampling formula. Emphasis will be given to the Markovian case, in particular for discretely observed Ornstein-Uhlenbeck diffusion processes. Some properties of the resulting model are explained and posterior results are obtained via a novel Markov chain Monte Carlo algorithm.

Article information

Source
Bayesian Anal., Volume 9, Number 4 (2014), 823-858.

Dates
First available in Project Euclid: 21 November 2014

Permanent link to this document
https://projecteuclid.org/euclid.ba/1416579181

Digital Object Identifier
doi:10.1214/14-BA878

Mathematical Reviews number (MathSciNet)
MR3293958

Zentralblatt MATH identifier
1327.62450

Keywords
Bayesian nonparametric Change point detection Ornstein-Uhlenbeck process Two-parameter Poisson-Dirichlet process

Citation

Martínez, Asael Fabian; Mena, Ramsés H. On a Nonparametric Change Point Detection Model in Markovian Regimes. Bayesian Anal. 9 (2014), no. 4, 823--858. doi:10.1214/14-BA878. https://projecteuclid.org/euclid.ba/1416579181


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