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 Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
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 |
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Conference Date | 2003/7/21(1days) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
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 |
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