Dynamic detection of change points in long time series

被引：40
|
作者：
Chopin, Nicolas [1 ]
机构：
[1] Univ Bristol, Sch Math, Bristol BS8 1TW, Avon, England
来源：
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 2007年 / 59卷 / 02期
关键词：
change point models; GARCH models; Markov chain Monte Carlo; particle filter; sequential Monte Carlo; state state models;
D O I：
10.1007/s10463-006-0053-9
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We consider the problem of detecting change points (structural changes) in long sequences of data, whether in a sequential fashion or not, and without assuming prior knowledge of the number of these change points. We reformulate this problem as the Bayesian filtering and smoothing of a non standard state space model. Towards this goal, we build a hybrid algorithm that relies on particle filtering and Markov chain Monte Carlo ideas. The approach is illustrated by a GARCH change point model.
引用
收藏
页码：349 / 366
页数：18
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