IMPROVING BAYESIAN ESTIMATION OF THE END POINT OF A DISTRIBUTION
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- Minoda Yuta
- Graduate School of Science and Engineering, Chuo University
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- Kamakura Toshinari
- Faculty of Science and Engineering, Chuo University
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- Yanagimoto Takemi
- Faculty of Science and Engineering, Chuo University
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Abstract
Bayesian estimation of the end point of a distribution is proposed and examined. For this problem, it is well known that the maximum likelihood method does not work well. By modifying the prior density in Hall and Wang (2005) and applying marginal inference, we derive estimators superior to existing ones. The proposed estimators are closely related to the estimating functions which are known to outperform maximum likelihood equations. Another advantage of the proposed method is to resolve the convergence problem. Our simulation results strongly support the superiority of the proposed estimators over the existing ones under the mean squared error. Illustrative examples are also given.
Journal
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- Journal of the Japanese Society of Computational Statistics
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Journal of the Japanese Society of Computational Statistics 22 (1), 79-91, 2009
Japanese Society of Computational Statistics
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Keywords
Details 詳細情報について
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- CRID
- 1390001204414953216
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- NII Article ID
- 110007502780
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- NII Book ID
- AA10823693
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- ISSN
- 18811337
- 09152350
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed