2004 Fiscal Year Final Research Report Summary
Research on an integration methodology for combining many competing models with a dynamical mixing and/or switching
| Project/Area Number |
14208025
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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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 |
HIGUCHI Tomoyuki The Institute of Statistical Mathematics, Department of Prediction and Control, Prof., 予測制御研究系, 教授 (70202273)
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| Co-Investigator(Kenkyū-buntansha) |
KITAGAWA Genshiro The Institute of Statistical Mathematics, Director-General, 所長 (20000218)
TAMURA Yoshiyasu The Institute of Statistical Mathematics, Center for Development of Statistical Computing, Prof., 統計計算開発センター, 教授 (60150033)
SATO Seisho The Institute of Statistical Mathematics, Department of Prediction and Control, Assoc. Prof., 予測制御研究系, 助教授 (60280525)
KAWASAKI Yoshinori The Institute of Statistical Mathematics, Department of Prediction and Control, Assist. Prof., 予測制御研究系, 助手 (70249910)
KAWANO Hideaki The Institute of Statistical Mathematics, Faculty of Sciences, Kyushu University, Assist. Prof., 理学研究院, 助教授 (60304721)
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| Project Period (FY) |
2002 – 2004
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| Keywords | Model averaging / Markov switching / DNA microarray / target tracking / Data mining / high frequency financial data / physical random number |
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| Research Abstract |
We studied an integration method of the empirical Bayesian and pseudo-Bayesian procedures and concentrated on developing information criteria to choose the best meta-model each of which is built up by combining many competing models to enhance a performance on the predictive ability. We made an effort to develop the new methods for resampling procedures in a framework of the sequential Monte Carlo methods that has drawn many interests in Bayesian computation. A theoretical work has been also studied by investigating the similarities in algorithms between the model averaging approach and boosting in Artificial Intelligence community. We considered an application of the procedures in which a mixing and/or switching mechanisms of many competing models plays an important role in boosting the predictive ability as well as the flexibility while keeping the robustness. As for the concrete examples of application, we have conducted the research projects on an automatic transaction of signals o
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btained by the artificial satellites, an analysis of high-frequent financial data, automatic target tracking and recognition for vision and sensor fusion technique, analysis of Point of Sales (POS) data, and time-dependent inversion from GPS network data. We confirmed an effectiveness of the proposed algorithms for an automatic creation of models by applying this methodology to real world problems, and attacked the new challenging problems in response to the social demand. In particular, we developed the new analysis technique for DNA array data that measures the expression level of thousands of genes in a single experiment in genome science. In the analysis of DNA array data, we are faced with difficulties most of which arise from the fact such that while the number of cases (sample size) is at most 100, a dimension of the feature vector (number of genes) ranges from several thousands up to a few ten thousands. It is therefore impossible to apply the conventional clustering methods, because of over-learning. To overcome such difficulty, we developed a novel clustering method, and provided a software called ArrayCluster which can be downloaded at http://www.ism.ac.jp/〜higuchi/arraycluster.htm. We organized an international symposium on advancement in the statistical modeling methodology, "Science of Modeling : The 30^<th> Anniversary of the Information Criterion (AIC2003)" for promoting the Science of Modeling. Less
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