A0577
Title: Change point detection using copula-based Markov chain models
Authors: Li-Hsien Sun - National Central University (Taiwan) [presenting]
Ming-Hua Hsieh - National Chengchi University (Taiwan)
Chi-Yang Chiu - University of Tennessee Health Science Center (United States)
Dong-Hua Kuo - National Central University (Taiwan)
Yu Kai Wang - Graduate Institute of Statistics of National Central University (Taiwan)
Abstract: The copula-based Markov chain models are considered for describing the sequential data with nonlinear features. In particular, the focus is on the detection of structural change in the obtained data. The proposed problem is very important in real application such as finance, industry, and biology. In order to tackle this issue, the Bayesian online algorithm is applied to the online change point detection. The performance of the proposed methods is illustrated through the simulation studies and empirical studies. The obtained results are also compared with the offline method using the maximum likelihood method.
