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December 2015 Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models
Degui Li, Yuan Ke, Wenyang Zhang
Ann. Statist. 43(6): 2676-2705 (December 2015). DOI: 10.1214/15-AOS1356

Abstract

In this paper, we study the model selection and structure specification for the generalised semi-varying coefficient models (GSVCMs), where the number of potential covariates is allowed to be larger than the sample size. We first propose a penalised likelihood method with the LASSO penalty function to obtain the preliminary estimates of the functional coefficients. Then, using the quadratic approximation for the local log-likelihood function and the adaptive group LASSO penalty (or the local linear approximation of the group SCAD penalty) with the help of the preliminary estimation of the functional coefficients, we introduce a novel penalised weighted least squares procedure to select the significant covariates and identify the constant coefficients among the coefficients of the selected covariates, which could thus specify the semiparametric modelling structure. The developed model selection and structure specification approach not only inherits many nice statistical properties from the local maximum likelihood estimation and nonconcave penalised likelihood method, but also computationally attractive thanks to the computational algorithm that is proposed to implement our method. Under some mild conditions, we establish the asymptotic properties for the proposed model selection and estimation procedure such as the sparsity and oracle property. We also conduct simulation studies to examine the finite sample performance of the proposed method, and finally apply the method to analyse a real data set, which leads to some interesting findings.

Citation

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Degui Li. Yuan Ke. Wenyang Zhang. "Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models." Ann. Statist. 43 (6) 2676 - 2705, December 2015. https://doi.org/10.1214/15-AOS1356

Information

Received: 1 January 2015; Revised: 1 June 2015; Published: December 2015
First available in Project Euclid: 7 October 2015

zbMATH: 1327.62262
MathSciNet: MR3405608
Digital Object Identifier: 10.1214/15-AOS1356

Subjects:
Primary: 62G08
Secondary: 62G20

Keywords: GSVCM , Lasso , local maximum likelihood , oracle estimation , SCAD , Sparsity , ultra-high dimension

Rights: Copyright © 2015 Institute of Mathematical Statistics

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Vol.43 • No. 6 • December 2015
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