Constructing estimators based on orthogonal components in a parametric model
| Project/Area Number |
10680325
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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 |
YANAGIMOTO Takemi The Institute of Statistical Mathematics, Department of Interdisciplinary Statistics, Professor, 領域統計研究系, 教授 (40000195)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 1999: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1998: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Kullback-Leibler separator / Orthogonality / Simultaneous estimation / Logarithic link function / Stein estimator / 制約付最尤推定量 |
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| Research Abstract |
The notion of orthogonality among components of a parameter is developed in relation with the estimation of the parameter. The promising situation are : 1) The dimension of a parameter high and 2) Distribution functions are parameterized in a favorable way. These restrictions may sound severely restrictive, but flexible models can be constructed in practice under this restriction.
Among many interesting problems the present research focused on the two subjects ; the simultaneous estimation of a high-dimensional mean parameter and the regression model with emphasis on the logarithmic link function. Several results relating the former subject were successfully obtained. In addition, a close relation with Bayesian approach has been called our attention. This finding will be significantly benefited for our subsequent researches. Specific results are concerned with 1) an unexpected application of the two estimating equation (to be appeared in SPL), 2) an application of the ordinal differential equation (to be appeared in CIS), 3) notions of orthogonality (Manuscript) and dual conjugate prior (in preparation). The three manuscripts except for the last are filed in the report of this research. Although we obtained some results on the latter subject of the regression model. The results are still below the satisfactory level.
The present subject is obviously related with the most attractive subjects in both the theory and application of statistics. All the enthusiastic methods such as estimating equations, smoothing methods and separate inference are expected to owe the efficient use of the notion of orthogonality. It is my hope that the present research made a significant contribution on enhancing the research in this line.
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Report
(3 results)
Research Products
(14 results)
