Presentation 2010-01-19
An Estimator Based on Empirical Likelihood and Its Properties
Manabu TAKEDA, Kazushi IKEDA, Takashi TAKENOUCHI, Yuki YOSHIDA,
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Abstract(in English) Empirical Likelihood is a nonparametric statistical inference method. It is applied mainly to statistical test and construction of confidence intervals and not studied for statistical estimation. In this study, we show that EL can be applied to estimation problems with side information. Furthermore, by extending the geometrical aspect of EL, we propose an estimation method based on Entropy Maximization. Two methods differ only in the direction of Kullback-Leibler divergence, but the former has many estimators, while the latter is ensured to have only one estimator. We compare these performances in computer simulations with Maximum Likelihood estimators and sample averaged estimators. Consequently, two methods were shown to have comparable performances.
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Keyword(in English) Empirical Likelihood / Side Information / KL divergence / Entropy Maximization / Estimating Equation
Paper # NC2009-84
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Committee NC
Conference Date 2010/1/11(1days)
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Registration To Neurocomputing (NC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An Estimator Based on Empirical Likelihood and Its Properties
Sub Title (in English)
Keyword(1) Empirical Likelihood
Keyword(2) Side Information
Keyword(3) KL divergence
Keyword(4) Entropy Maximization
Keyword(5) Estimating Equation
1st Author's Name Manabu TAKEDA
1st Author's Affiliation Kyoto University()
2nd Author's Name Kazushi IKEDA
2nd Author's Affiliation Nara Institute of Science and Technology
3rd Author's Name Takashi TAKENOUCHI
3rd Author's Affiliation Nara Institute of Science and Technology
4th Author's Name Yuki YOSHIDA
4th Author's Affiliation Osaka University
Date 2010-01-19
Paper # NC2009-84
Volume (vol) vol.109
Number (no) 363
Page pp.pp.-
#Pages 5
Date of Issue