On a Nonparametric Change Point Detection Model in Markovian Regimes

被引：22
|
作者：
Fabian Martinez, Asael [1 ]
Mena, Ramses H. [1 ]
机构：
[1] Univ Nacl Autonoma Mexico, Mexico City 04510, DF, Mexico
来源：
BAYESIAN ANALYSIS | 2014年 / 9卷 / 04期
关键词：
Bayesian nonparametric; Change point detection; Ornstein-Uhlenbeck process; Two-parameter Poisson-Dirichlet process; PRODUCT PARTITION MODEL; BAYESIAN-ANALYSIS; TIME-SERIES; PROBABILITY; INFERENCE; SEQUENCE;
D O I：
10.1214/14-BA878
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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.
引用
收藏
页码：823 / 857
页数：35
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