Two-step variable selection in partially linear additive models with time series data. Issue 3 (16th March 2018)

• Record Type: Journal Article
• Authors: Feng, Mu; Chen, Zhao; Cheng, Ximing
• Is Part Of: Communications in statistics. Volume 47:Issue 3(2018)
• Page Start: 661
• Page End: 671
• Publication Date: 2018-03-16
• Subjects: Adaptive LASSO -- Additive models -- GCV -- Group LASSO -- Orthogonal series approximation -- Variable selection; 62-07 -- 62G08 -- 62G05; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1259477
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Lots of semi-parametric and nonparametric models are used to fit nonlinear time series data. They include partially linear time series models, nonparametric additive models, and semi-parametric single index models. In this article, we focus on fitting time series data by partially linear additive model. Combining the orthogonal series approximation and the adaptive sparse group LASSO regularization, we select the important variables between and within the groups simultaneously. Specially, we propose a two-step algorithm to obtain the grouped sparse estimators. Numerical studies show that the proposed method outperforms LASSO method in both fitting and forecasting. An empirical analysis is used to illustrate the methodology.