IMPROVING BAYESIAN ESTIMATION OF THE END POINT OF A DISTRIBUTION

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概要

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• 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.

• 日本計算機統計学会の論文

著者

• Kamakura Toshinari

  Faculty Of Science And Engineering Chuo University

• Minoda Yuta

  Graduate School of Science and Engineering, Chuo University

• Yanagimoto Takemi

  Faculty of Science and Engineering, Chuo University

• Yanagimoto Takemi

  Faculty Of Science And Engineering Chuo University

• Minoda Yuta

  Graduate School Of Science And Engineering Chuo University

関連論文

• IMPROVING BAYESIAN ESTIMATION OF THE END POINT OF A DISTRIBUTION

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