Model averaging marginal regression for high dimensional conditional quantile prediction

In this article, we propose a high dimensional semiparametric model average approach to predict the conditional quantile of the response variable. Firstly, we approximate the multivariate conditional quantile function by an affine combination of one-dimensional marginal conditional quantile functions which can be estimated by the local linear regression. Secondly, based on the estimated marginal quantile regression functions, a penalized quantile regression is proposed to estimate and select the significant model weights involved in the approximation. Under some mild conditions, we have established the asymptotic properties for both the parametric and nonparametric estimators. Finally, we evaluate the finite sample performance of the proposed procedure via simulations and a real data analysis.

Automated summary

This article introduces a high-dimensional semiparametric model averaging approach for predicting the conditional quantile of the response variable. By estimating and selecting important model weights, the proposed method evaluates the finite sample performance through simulations and real data analysis.