| Project/Area Number |
11308010
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (A)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Statistical science
|
|---|
| Research Institution | Hokkaido University |
|---|
Principal Investigator |
SATO Yoshiharu Hokkaido Univ., Div. Systems & Information Eng., Professor, 大学院・工学研究科, 教授 (80091461)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
IMAI Hideyuki Hokkaido Univ., Div. Systems & Information Eng., Assoc. Prof., 大学院・工学研究科, 助教授 (10213216)
MURAI Tetsuya Hokkaido Univ., Div. Systems & Information Eng., Assoc. Prof., 大学院・工学研究科, 助教授 (90201805)
MIZUTA Masahiro Hokkaido Univ., Center for Infor. & Multimedia Stud., Professor, 情報メディア教育研究総合センター, 教授 (70174026)
TANIEICHI Nobuhiro Obihiro Univ. of Agriculture & Veterinary Medicine, Professor, 畜産学部, 教授 (00207200)
BABA Yasumasa Institute of Statistical Mathematics, Professor, 教授 (90000215)
山本 義郎 北海道大学, 大学院・工学研究科, 助手 (80301943)
|
|---|
| Project Period (FY) |
1999 – 2002
|
| Project Status |
Completed (Fiscal Year 2002)
|
| Budget Amount *help |
¥30,790,000 (Direct Cost: ¥27,100,000、Indirect Cost: ¥3,690,000)
Fiscal Year 2002: ¥10,010,000 (Direct Cost: ¥7,700,000、Indirect Cost: ¥2,310,000)
Fiscal Year 2001: ¥5,980,000 (Direct Cost: ¥4,600,000、Indirect Cost: ¥1,380,000)
Fiscal Year 2000: ¥7,200,000 (Direct Cost: ¥7,200,000)
Fiscal Year 1999: ¥7,600,000 (Direct Cost: ¥7,600,000)
|
|---|
| Keywords | Data Science / Statistical Model / Statistical Inference / Model Selection / Robustness of Models / Forecasting / Data Mining |
|---|
| Research Abstract |
The aim of this project was to study out the possibilities and limits of statistical prediction in the practical problems. There are two situations in the statistical prediction. One is an interpolation and the other is an extrapolation. The typical concept of the former, interpolation, is a theory of regression, namely, the nonlinear, semi-parametric and nonparametric regression. Moreover, the several modeling techniques by which the reasonable and useful information are extracted have been included. On these methods, we get excellent results, for instance, "growth curve model with hierarchcal within-individual design matrices", "echelon analysis of the relationship between population and land cover patterns based on remote sensing data", "an algorithm with projection pursuit for sliced inverse regression model", "Kullback-Leibler information consistent estimator for censored data" and so on. On the other hand, the latter concept, extrapolation, is familiar to weather or a business or a stock price forecasting. The typical method is a time series analysis. Although, there still remain many dfficulties, in this project, many useful results are proposed, especially on the state space modeling a relaxation of error distribution and several filtering theory for the noise reduction. For example, "computational implementations of nonlinear non-Gaussian prediction and filtering formulas", "a combining forecast method using a probabilistic neural network", "forecasting structural changes in a state-space model", "extension of the MDL criterion by shrinkage method" and so on.
|
