Presentation 2003/7/16
A Study of Modeling of Nonstationary Time Series based on Poisson Distribution
Kinya IWATA, Takahiro YOSHIDA, Toshiyasu MATSUSHIMA,
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Abstract(in English) Gaussian and linear state space model is a model for nonstationary time series. When we deal with non-Gaussian data based on the poisson distribution, however, this model is not adequate for the analysis. So some approaches to extend Gaussian and linear state space model have been proposed. For example, West et al proposed the Dynamic Generalized Linear Model. One of problems on his model is that exact posterior distribution of the parameter can not be obtained. In this paper, we propose model that enable us to calculate exact posterior distribution of the parameter analytically. Additionally, we show some simulataion results to make sure properties of this model.
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Keyword(in English) Time series analysis / Poisson distribution / Nonstationary / Bayes estimation
Paper # IT2003-39
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Conference Date 2003/7/16(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Study of Modeling of Nonstationary Time Series based on Poisson Distribution
Sub Title (in English)
Keyword(1) Time series analysis
Keyword(2) Poisson distribution
Keyword(3) Nonstationary
Keyword(4) Bayes estimation
1st Author's Name Kinya IWATA
1st Author's Affiliation Dep. of Industrial and Management Systems Engineering Waseda University()
2nd Author's Name Takahiro YOSHIDA
2nd Author's Affiliation Dep. of Industrial and Management Systems Engineering Waseda University
3rd Author's Name Toshiyasu MATSUSHIMA
3rd Author's Affiliation Dep. of Industrial and Management Systems Engineering Waseda University
Date 2003/7/16
Paper # IT2003-39
Volume (vol) vol.103
Number (no) 215
Page pp.pp.-
#Pages 6
Date of Issue