Presentation 2003/7/21
Estimation of conditional mean by the linear combination of quantile regression under heteroscedastic asymmetric errors
Takafumi KANAMORI, Ichiro TAKEUCHI,
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Abstract(in English) We investigate regression problems when the error distributions are asymmetric and heavy-tail. If the error distribution is symmetric around the mean value, traditional robust estimators are helpful by reducing the effect of outliers equally from both sides of the distribution. Under asymmetric heavy-tail error distribution, however, those estimators are biased. We suggest a robust estimator which consists of the linear combination of quantile regressions. The estimator is derived from generalized location scale and we show the robustness of the suggested estimator theoretically. Numerical experiments confirm the clear advantage of the suggested estimator comparing to traditional ones.
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Keyword(in English) quantile regression / robust estimation / asymmetric heavy-tail distribution / heteroscedasticity / generalized location scale model
Paper # NC2003-29
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Committee NC
Conference Date 2003/7/21(1days)
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Language JPN
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Title (in English) Estimation of conditional mean by the linear combination of quantile regression under heteroscedastic asymmetric errors
Sub Title (in English)
Keyword(1) quantile regression
Keyword(2) robust estimation
Keyword(3) asymmetric heavy-tail distribution
Keyword(4) heteroscedasticity
Keyword(5) generalized location scale model
1st Author's Name Takafumi KANAMORI
1st Author's Affiliation Dept. of Mathematical and Computing Sciences, Tokyo Institute of Technology()
2nd Author's Name Ichiro TAKEUCHI
2nd Author's Affiliation Dept. of Information Engineering, Faculty of Engineering, Mie University
Date 2003/7/21
Paper # NC2003-29
Volume (vol) vol.103
Number (no) 227
Page pp.pp.-
#Pages 6
Date of Issue