B0754
Title: Semiparametric quantile regression
Authors: Anneleen Verhasselt - Hasselt University (Belgium) [presenting]
Abstract: Quantile regression is an important tool in data analysis. Linear regression, or more generally, parametric quantile regression often imposes too restrictive assumptions. Nonparametric regression avoids making distributional assumptions but might have the disadvantage of not exploiting distributional modeling elements that might be brought in. A semiparametric approach towards estimating conditional quantile curves is proposed. It is based on a recently studied large family of asymmetric densities of which the location parameter is a quantile (and not a mean). Passing to conditional densities and exploiting local likelihood techniques in a multiparameter functional setting then leads to a semiparametric estimation procedure.
