| Project/Area Number |
63830002
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| Research Category |
Grant-in-Aid for Developmental Scientific Research (B).
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| Allocation Type | Single-year Grants |
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| Research Field |
統計学
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
KITAGAWA Genshiro The Institute of Statistical Mathematics, Department of Prediction and Central Associate Professor, 予測制御研究系, 助教授 (20000218)
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| Co-Investigator(Kenkyū-buntansha) |
HIGUCHI Tomoyuki The Institute of Statistical Mathematics, Department of Prediction and Control A, 予測制御研究系, 助手 (70202273)
IBA Yukito The Institute of Statistical Mathematics, Statistical Data Analysis Center Assis, 統計データ解析センター, 助手 (30213200)
SAKAMOTO Yoshiyuki The Institute of Statistical Mathematics, Department of Interdisciplinary Statis, 領域統計研究系, 助教授 (50000211)
TAMURA Yoshiyasu The Institute of Statistical Mathematics, Statistical Data Analysis Center Assoc, 統計データ解析センター, 助教授 (60150033)
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| Project Period (FY) |
1988 – 1990
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| Project Status |
Completed (Fiscal Year 1990)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,900,000)
Fiscal Year 1990: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1989: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 1988: ¥2,300,000 (Direct Cost: ¥2,300,000)
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| Keywords | Time Series Analysis / Non-stationarity / Non-linearity / Smoothing / Filtering / Statistical Software / フィルタ- / フィルタ / 非線形システム / 状態空間モデル / カルマンフィルター / AIC |
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| Research Abstract |
By the research in the previous fiscal years, we have developed a recursive filtering and smoothing algorithms for a general state space model which can be applied to diverse fields of time series modeling.
Based on these results, in this year, we tested the developed program on various real data sets and made necessary modifications. We also did research on developing new models and more efficient algorithms. As a result of this research, based on the non-Gaussian state space model, a unified method fr analyzing time series with various characters such as the non-linearity, non-stationarity, non-Gaussianity, outliers and missing observations has been established. We also did research on the graphical display of the results of time series analysis and developed various programs for that purpose. In particular, by using the CALCOMP graphic libraries it becomes possible to use the graphical programs on various kinds of computers including mainframes and personal computers.
As mentioned above, in this research a statistical model for handling time series with various character and a softrware for implementing the approach have been developed. Therefore, we believe that the most part of our purpose of this research has been achieved. However, more research is required to develop a user-friendly practical software.
The papers and other publications produced concerning this research have been included in the report of this research project. A manual for the developed software is also available.
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