Quickly variable selection for varying coefficient models with missing response at random

ABSTRACT

This paper focuses on the variable selections for a varying coefficient models with missing response at random. A procedure is presented by basis function approximations with smooth-threshold estimating equations. Furthermore, the proposed method selects significant variables and estimates coefficients simultaneously avoiding the problem of solving a convex optimization, which reduced the burden of computation. Compared to existing equation based approaches, our procedure is more efficient and quick. With proper choices the regularization parameter, the resulting estimates perform an oracle property. A cross-validation for tuning parameter selection is also proposed, a numerical study confirms the performance of the proposed method.

KEYWORDS:

• Smooth-threshold estimating equations
• Varying coefficient model
• Variable selection

MATHEMATICS SUBJECT CLASSIFICATION:

• 62G08
• 62G20

Additional information

Funding

Jian Wu's research was supported by the Doctoral Scientific Research Foundation of Liaoning Province (No. 201601009) and the Grant of Central Universities of China (No. N160504004). Peixin Zhao's research was supported by the Chongqing Research Program of Basic Theory and Advanced Technology (cstc2016jcyjA0151).