WEKO3
アイテム
Multivariate Times Series Analysis of Heteroscedastic Date, with Application to Neuroscience
https://ir.soken.ac.jp/records/774
https://ir.soken.ac.jp/records/7749f9b1897-872b-4230-975e-ee2d9c7cefac
| 名前 / ファイル | ライセンス | アクション |
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要旨・審査要旨 (158.8 kB)
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本文 (7.9 MB)
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| Item type | 学位論文 / Thesis or Dissertation(1) | |||||
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| 公開日 | 2010-02-22 | |||||
| タイトル | ||||||
| タイトル | Multivariate Times Series Analysis of Heteroscedastic Date, with Application to Neuroscience | |||||
| タイトル | ||||||
| タイトル | Multivariate Times Series Analysis of Heteroscedastic Date, with Application to Neuroscience | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_46ec | |||||
| 資源タイプ | thesis | |||||
| 著者名 |
王, 健歡
× 王, 健歡 |
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| フリガナ |
オウ, ケンカン
× オウ, ケンカン |
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| 著者 |
WONG, Kin Foon Kevin
× WONG, Kin Foon Kevin |
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| 学位授与機関 | ||||||
| 学位授与機関名 | 総合研究大学院大学 | |||||
| 学位名 | ||||||
| 学位名 | 博士（学術） | |||||
| 学位記番号 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 総研大甲第948号 | |||||
| 研究科 | ||||||
| 値 | 複合科学研究科 | |||||
| 専攻 | ||||||
| 値 | 15 統計科学専攻 | |||||
| 学位授与年月日 | ||||||
| 学位授与年月日 | 2006-03-24 | |||||
| 学位授与年度 | ||||||
| 値 | 2005 | |||||
| 要旨 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | This thesis summarizes statistical analysis of some multivariate heteroscedastic<br /> time series data, including 2 sets of data from physiological experiments and<br /> 2 sets of EEG data about anaesthesia and coma.<br /> The aim of this thesis is to provide a statistical tool for analyzing multi-<br />variate data which contains non-stationary and heteroscedastic characteristics.<br /> The main contribution of this thesis is that we combine the linear state<br /> space model and GARCH model to develop a state space-GARCH model.<br />The state space-GARCH model can describe the non-stationary characteristics<br /> of the system noise variance. In particular we adopt a special structure of<br /> the linear state space model to decompose a data into components by their<br /> frequencies. Combining a heteroscedasticity model and a state space model<br /> is carried out by fully utilizing the information of innovations and expected<br /> values from the filtering process.<br /> Another contribution of the thesis is that we extend Akaike's NCR from<br /> constant noise variance to heterogeneous noise variance in order to study time-<br />varying causality. By applying heteroscedasticity models, the phenomenon of<br /> an evolving causality relationship can be depicted.<br /> All these methods are illustrated by their application to EEG data including<br /> the study of consciousness under anaesthesia and coma, and also to a physical<br /> data of head and finger movement. | |||||
| 所蔵 | ||||||
| 値 | 有 | |||||
