Quantile index coefficient model with variable selection

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• 作者：Weihua Zhao^a ; Heng Lian^b ; ^{heng.lian@unsw.edu.au}
• 关键词：62G08 ; G2G20
• 刊名：Journal of Multivariate Analysis
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：154
• 期：Complete
• 页码：40-58
• 全文大小：541 K
• 卷排序：154

文摘

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 (αα-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.