Semiparametric estimation of additive quantile regression models by two-fold penalty
Date of Issue2012
School of Physical and Mathematical Sciences
In this article, we propose a model selection and semiparametric estimation method for additive models in the context of quantile regression problems. In particular, we are interested in finding nonzero components as well as linear components in the conditional quantile function. Our approach is based on spline approximation for the components aided by two Smoothly Clipped Absolute Deviation (SCAD) penalty terms. The advantage of our approach is that one can automatically choose between general additive models, partially linear additive models, and linear models in a single estimation step. The most important contribution is that this is achieved without the need for specifying which covariates enter the linear part, solving one serious practical issue for models with partially linear additive structure. Simulation studies as well as a real dataset are used to illustrate our method.
Journal of business & economic statistics