Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79568
Authors: 
Fan, Yan
Härdle, Wolfgang Karl
Wang, Weining
Zhu, Lixing
Year of Publication: 
2013
Series/Report no.: 
SFB 649 Discussion Paper 2013-010
Abstract: 
Quantile regression is in the focus of many estimation techniques and is an important tool in data analysis. When it comes to nonparametric specifications of the conditional quantile (or more generally tail) curve one faces, as in mean regression, a dimensionality problem. We propose a projection based single index model specification. For very high dimensional regressors X one faces yet another dimensionality problem and needs to balance precision vs. dimension. Such a balance may be achieved by combining semiparametric ideas with variable selection techniques.
Subjects: 
quantile single-index regression
minimum average contrast estimation
co-VaR estimation
composite quasi-maximum likelihood estimation
Lasso
model selection
Document Type: 
Working Paper

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