| Item type |
学位論文 / Thesis or Dissertation(1) |
| 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
|
| フリガナ |
オウ, ケンカン
|
| 著者 |
WONG, Kin Foon Kevin
|
| 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 |
要旨・審査要旨 (158.8 kB)
