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Activity Number: 355
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #316325 View Presentation
Title: Efficient Estimation of Quantile Regression via Semiparametric Mixture Model
Author(s): Hiroyuki Taniai*
Companies: Waseda University
Keywords: Quantile Regression ; semiparametrics ; efficient influence operator
Abstract:

The random coefficient interpretation of Quantile Regression (QR) model, e.g., Koenker and Xiao (2006, JASA), makes us realize that the parameter of interest is a function of uniform random variables. So, this is the case of Banach-valued parameters. By regarding this model further as a kind of semiparametric mixture model, I derived the tangent spaces and obtained the efficient influence operator for QR coefficient functions. In this paper I will provide the one-step estimator which attains the efficiency bound, based on this efficient influence operator. This efficiency bound can be presented in terms of the usual QR models such as location, scale, location-scale etc., since they are the parametric submodels and its reparametrizations. Some applications will be presented as well.


Authors who are presenting talks have a * after their name.

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