Estimation for Single-Index mixed models with Longitudinal data

2010/04/04 by Zhen Pang, Liugen Xue, Pang, Zhen +1
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1004.0482

openalex publication_date 2010/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we consider a single-index mixed model with longitudinal data. A new set of estimating equations is proposed to estimate the single-index coefficient. The link function is estimated by using the local linear smoothing. Asymptotic normality is established for the proposed estimators. Also, the estimator of the link function achieves optimal convergence rates; and the estimators of variance components have root-n consistency. These results facilitate the construction of confidence regions/intervals and hypothesis testing for the parameters of interest. Some simulations and an application to real data are included.

Citations

• Longitudinal data analysis using generalized linear models
• Smoothing noisy data with spline functions

Related

• Estimation for a Partial-Linear Single-Index Model
• Efficient Estimation in Single Index Models through Smoothing splines
• Simultaneous semiparametric inference for single-index models
• Variable Selection and Estimation for Partially Linear Single-index Models with Longitudinal Data
• High-Dimensional Single-Index Models: Link Estimation and Marginal Inference