Quantile index coefficient model with variable selection

JOURNAL OF MULTIVARIATE ANALYSIS

卷:154

Article

Zhao, Weihua¹  Lian, Heng² 

[1] Nantong Univ, Sch Sci, Nantong 226019, Peoples R China

[2] City Univ Hong Kong, Dept Math, Hk 999077, Hong Kong, Peoples R China

关键词: Asymptotic normality;    B-splines;    Check loss minimization;    Mixing condition;    Variable selection;   

DOI  :  10.1016/j.jmva.2016.10.009

来源: Elsevier

【 摘 要 】

We consider conditional quantile estimation in functional index coefficient models for time series data, using regression splines, which gives more complete information on the conditional distribution than the conditional mean model. An important technical aim is to demonstrate the faster rate and asymptotic normality of the parametric part, which is achieved through an orthogonalization approach. For this class of very flexible models, variable selection is an important problem. We use smoothly clipped absolute deviation (SCAD) penalty to select either the covariates with functional coefficients, or covariates that enter the index, or both. We establish the oracle property of the penalization method under strongly mixing (alpha-mixing) conditions. Simulations are carried out to investigate the finite-sample performance of estimation and variable selection. A real data analysis is reported to demonstrate the application of the proposed methods. (C) 2016 Elsevier Inc. All rights reserved.

【 授权许可】

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