EMPIRICAL REGRESSION QUANTILE
スポンサーリンク
概要
- 論文の詳細を見る
This study proposes a new use of goal programming for empirically estimating a, regression quantile hyperplane. The approach can yield regression quantile estimates that are less sensitive to not only non-Gaussian error distributions but also a small sample size than conventional regression quantile methods. The performance of regression quantile estimates is compared wish least absolute value estimates in a simulation study.
- 社団法人日本オペレーションズ・リサーチ学会の論文
著者
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Sueyoshi Toshiyuki
Faculty of Science and Engineering, Science University of Tokyo
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Sueyoshi T
Sci. Univ. Tokyo
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Sueyoshi Toshiyuki
The Ohio State University College Of Business
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