Research on implementations of computer-intensive selection methods for regularized statistical models
| Project/Area Number |
17500189
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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 |
KAWASAKI Yoshinori The Institute of Statistical Mathematics, Department of Statistical Modeling, Associate Professor (70249910)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,070,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥270,000)
Fiscal Year 2007: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2006: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2005: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | Information criteria / Regularization / Time Series Analysis / Model Selection / Term Structure of Interest Rates / GARCH model / Risk Management / Intervention Analysis / 状態空間モデル / ロジットモデル / オンライン学習システム / 多変量GARCHモデル / 周辺化尤度 / 一般化動的線型モデル / ブートストラップ / 時系列モデル |
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| Research Abstract |
(1) In nonparametric estimation of term structures of interest rates from traded bond data, it is empirically well-known that approximating forward rate often yields better result. The problem of this approach is, however, that the use of generalized cross-validation cannot be justified to choose regularization parameters. In this project, we established a version of generalized information criteria (GIC) that holds theoretical validity in the determination of regularization parameter. (2) We compared the performance of various types of GARCH models and multivariate GARCH models in terms of the coherency of downside risks, especially Value at Risk (VaR). Given the parameters estimated, we performed prediction simulation and compared the empirical exceedance rate with nominal size through a binomial test. Our conclusion is that Dynamic Conditional Correlation model performs best, together with its parsimonious parametric form. (3) Estimation of unobserved components time series models with intervention terms are studied. Data come from animal dose administration testing. These are time series data such as systolic blood pressure, diastolic blood pressure, heart rate and so on. We considered a time series model to decompose observation into trend, stationary autoregressive part and an exponential type intervention term, which enabled us to estimate acute toxicity through model selection. (4) Aiming disclosure of the research results to the public, online learning system on the web based time series analysis software was studied.
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Report
(4 results)
Research Products
(14 results)
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[Presentation] Statistical Courseware with Synchronized Web-Based Statistical Analysis System2007
Author(s)
Kanefuji, K., Kawasaki, Y., Sato, S., Sumiya, T., Ochi, Y.
Organizer
7th Hawaii International Conference on Statistics, Mathematics and Related Fields
Place of Presentation
Waikiki Beach Marriott Notel, Honolulu, Hawaii, U.S.A.
Year and Date
2007-01-17
Description
「研究成果報告書概要(欧文)」より
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