A new local estimation method for single index models for longitudinal data. Issue 3 (2nd July 2016)

• Record Type: Journal Article
• Authors: Lin, Hongmei; Zhang, Riquan; Shi, Jianhong; Liu, Jicai; Liu, Yanghui
• Is Part Of: Journal of nonparametric statistics. Volume 28:Issue 3(2016)
• Page Start: 644
• Page End: 658
• Publication Date: 2016-07-02
• Subjects: single index models -- Cholesky decomposition -- local linear regression -- longitudinaldata; Primary: 62G05 -- Secondary: 62E20; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2016.1191632
• Languages: English
• ISSNs: 1048-5252

Abstract

Abstract : Single index models are natural extensions of linear models and overcome the so-called curse of dimensionality. They are very useful for longitudinal data analysis. In this paper, we develop a new efficient estimation procedure for single index models with longitudinal data, based on Cholesky decomposition and local linear smoothing method. Asymptotic normality for the proposed estimators of both the parametric and nonparametric parts will be established. Monte Carlo simulation studies show excellent finite sample performance. Furthermore, we illustrate our methods with a real data example.