Grant-in-Aid for Scientific Research (B)
|Allocation Type||Single-year Grants|
|Research Institution||HIROSHIMA UNIVERSITY|
WAKAKI Hirofumi(2005) Hiroshima University, Graduate School of Science, Professor, 大学院・理学研究科, 教授 (90210856)
藤越 康祝(2003-2004) 広島大学, 大学院・理学研究科, 教授 (40033849)
FUJIKOSHI Yasunori Hiroshima University, Emeritus Professor, 名誉教授 (40033849)
KONISHI Sadanori Kyushu University, Graduate School of Mathematics, Professor, 大学院・数理学研究院, 教授 (40090550)
OHTAKI Megu Hiroshima University, Research Institute for Radiation Biology and Medicine, Professor, 原爆放射線医科学研究所, 教授 (20110463)
NISHII Ryuei Kyushu University, Graduate School of Mathematics, Professor, 大学院・数理学研究院, 教授 (40127684)
YANAGIHARA Hirokazu University of Tsukuba, Graduate School of Systems and Infiormation Engineering, Lecturer, 大学院・システム情報工学科, 講師 (70342615)
若木 宏文 広島大学, 大学院・理学研究科, 助教授 (90210856)
|Project Period (FY)
2003 – 2005
Completed(Fiscal Year 2005)
|Budget Amount *help
¥16,400,000 (Direct Cost : ¥16,400,000)
Fiscal Year 2005 : ¥5,000,000 (Direct Cost : ¥5,000,000)
Fiscal Year 2004 : ¥5,600,000 (Direct Cost : ¥5,600,000)
Fiscal Year 2003 : ¥5,800,000 (Direct Cost : ¥5,800,000)
|Keywords||Multivariate Analysis / Variable Selection / Multivariate Lenear Model / Growth Curve Model / Spatial Model / Nonlinear Model / Information Criterion / High Dimensional Data / 経時測定データ / AIC基準 / 高次元推測問題 / ベイズ型モデル評価基準 / ロジスティック回帰モデル|
The purpose of this project is to study variable selection problems for various models and related problems, and to apply to the actual data. Main results obtained are as follows.
(1) The likelihood ratio tests on a general linear hypothesis of the parameter matrix for an extended growth curve models (Fujikoshi et al. (2006)).
(2) Edgeworth expansion of Wilks's lambda statistic is derived for high dimensional case (Wakaki (2006)).
(3) Image classification methods are developed (Nishii et al. (2006)).
(4) An asymptotic distribution of the AIC is derived (Yanagihara et al. (2005)).
(5) Prediction error criterion for selecting of varaibles in regression model is proposed (Fujikoshi et al. (2005)).
(6) A Bayesian information criterion is derived and applied to choice of smoothing parameters in radial basis function network models (Konishi et al. (2004)).
(7) A clustering method by connected neighborhoods is proposed (Satoh et al. (2004)).
(8) A classification method for genotyping of Single nucleotide polymorphisms based on normal mixture model are proposed (Satoh et al. (2004)).
(9) A general bias reduction technique in the context of smooth functional statistics is develped and an criterion in model evaluation and selection problems is proposed (Konishi et al. (2003)).
(10) Some model selection criteria for grouth curve model are proposed (Fujikoshi (2003)).
(11) A bias correction method for AIC in logistic regression models is proposed (Yanagihara et al. (2003)).
(12) A method to select B-Spline and Polynomial regression is proposed (Satoh et al. (2003)).