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http://dx.doi.org/10.5351/KJAS.2009.22.4.735

A Fast Bayesian Detection of Change Points Long-Memory Processes  

Kim, Joo-Won (Office of Admissions, Seoul National University)
Cho, Sin-Sup (Department of Statistics, Seoul National University)
Yeo, In-Kwon (Department of Statistics, Sookmyoung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.22, no.4, 2009 , pp. 735-744 More about this Journal
Abstract
In this paper, we introduce a fast approach for Bayesian detection of change points in long-memory processes. Since a heavy computation is needed to evaluate the likelihood function of long-memory processes, a method for simplifying the computational process is required to efficiently implement a Bayesian inference. Instead of estimating the parameter, we consider selecting a element from the set of possible parameters obtained by categorizing the parameter space. This approach simplifies the detection algorithm and reduces the computational time to detect change points. Since the parameter space is (0, 0.5), there is no big difference between the result of parameter estimation and selection under a proper fractionation of the parameter space. The analysis of Nile river data showed the validation of the proposed method.
Keywords
ARFIMA models; change point detection; Dirichlet distribution;
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