Quantile regression with an epsilon-insensitive loss in a reproducing kernel Hilbert space
AbstractThis paper proposes a method to estimate the conditional quantile function using an epsilon-insensitive loss in a reproducing kernel Hilbert space. When choosing a smoothing parameter in nonparametric frameworks, it is necessary to evaluate the complexity of the model. In this regard, we provide a simple formula for computing an effective number of parameters when implementing an epsilon-insensitive loss. We also investigate the effects of the epsilon-insensitive loss.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 81 (2011)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Koenker,Roger, 2005.
Cambridge University Press, number 9780521608275, December.
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