A new local estimation method for single index models for longitudinal data

Hongmei Lin, Riquan Zhang, Jianhong Shi, Jicai Liu and Yanghui Liu

Journal of Nonparametric Statistics, 2016, vol. 28, issue 3, 644-658

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.

Date: 2016
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DOI: 10.1080/10485252.2016.1191632

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