Work

Variable selection for partially linear single-index varying-coefficient model

Year: 2025

Type: article

Abstract: .This article focuses on variable selection for a partially linear single-index varying-coefficient model. A variable selection method by employing the basis spline function approximations with SCAD penalty is proposed. It can select significant variables in the parametric and non parametric components and estimate the non zero regression coefficients and coefficient functions simultaneously. The consistency of the proposed method and the oracle property of the penalized least-squares estimators for high-dimensional data are established. Some simulations and the real data analysis are constructed to illustrate the finite sample performances of the proposed method.

Source: Communication in Statistics- Theory and Methods

DOI: 10.1080/03610926.2025.2581243

Authors: Lijuan Han, Liugen Xue, Junshan Xie

Institution: Henan University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Natural Science Foundation of Henan Province

Awards: G7600145278, G8806927890