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Variable selection for partially linear single-index varying-coefficient model
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DOI：10.1080/03610926.2025.2581243.png)
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Abstract
En 中文
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.
Keywords:
Partially linear single-index varying-coefficient model
variable selection
SCAD
high-dimensional data
Journal
C
IF:
0.8
Papers:
211
Citations:
0