Variable selection for partially varying coefficient model based on modal regression under high dimensional data

Abstract

In this article, we focus on the variable selection for partially varying coefficient model under high dimensional data. Variable selection is proposed based on modal regression estimation with bridge method. Using the B-spline basic function to approximate the non parametric function, a penalty estimation objective equation is constructed. It establishes and proves that the variable selection methods have oracle property. Numerical simulations tested the performance of the proposed methods in a finite sample and verified the significance of the proposed estimation and the variable selection methods.

KEYWORDS:

• Partially varying coefficient model
• high dimensional data
• modal regression
• variable selection

MATHEMATICS SUBJECT CLASSIFICATION:

• 62G05
• 62G08

Additional information

Funding

This work was supported by National Natural Science Foundation of China.