| Project/Area Number |
12680321
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | THE INSTITUTE OF STATISTICAL MATHEMATICS |
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Principal Investigator |
KITAGAWA Genshiro Dept. of Prediction and Control, The Institute of Statistical Mathematics, 予測制御研究系, 教授 (20000218)
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| Co-Investigator(Kenkyū-buntansha) |
KAWASAKI Yoshinori Dept. of Prediction andn Control, The Institute of Statistical Mathematics, Assistant Professor, 予測制御研究系, 助手 (70249910)
HIGUCHI Tomoyuki Dept. of Prediction andn Control, The Institute of Statistical Mathematics, Associate Professor, 予測制御研究系, 助教授 (70202273)
TAMURA Yoshiyasu Center for Development of Stat. Comp., The Institute of Statistcal Mathematics, Professor, 統計計算開発センター, 教授 (60150033)
SATO Seisho Dept. of Prediction andn Control, The Institute of Statistical Mathematics, Assistant Professor, 予測制御研究系, 助手 (60280525)
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| Project Period (FY) |
2000 – 2001
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| Keywords | General state-space model / Self-organization / Information criteria / Multivariate time series / Discovery science / Parallel computation |
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| Research Abstract |
Research upon this grant has been concerned in the following four fields ; (1) new methodologies in statistical modeling, (2) knowledge discovery based on statistical modeling, (3) application to actual data analysis and (4) software development. As for (1), resampling scheme in Monte Carlo filter is improved to stabilize the automatic model estimation via self-organizing state-space model. Also, several new method are tried to alleviate numerical diffculties often encountered in estimation of fixed parameters. (2) New algorithm for estimating Bayesian network is proposed which is designed for cDNA micro array data analysis. Minute by minute stock index data is analyzed by time series models, principal component analysis and cluster analysis to explore the calendar effect. (3) Based on multivariate time series model for ocean bottom seismograph data, an approximate space-time smoothing algorithm is proposed to estimate underground structure. Large-scale field-aligned currents are exhaustively analyzed, and automatic identification procedure is proposed. (4) Programs to estimate univariate and multivariate AR models are parallelized. This was done by the parallelization of Householder transformation. Filtering and smoothing algorithm for general state-space models is also implemented only on an experimental basis.
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