Electronic Journal of Statistics

Analysis of asynchronous longitudinal data with partially linear models

Li Chen and Hongyuan Cao

Full-text: Open access

Abstract

We study partially linear models for asynchronous longitudinal data to incorporate nonlinear time trend effects. Local and global estimating equations are developed for estimating the parametric and nonparametric effects. We show that with a proper choice of the kernel bandwidth parameter, one can obtain consistent and asymptotically normal parameter estimates for the linear effects. Asymptotic properties of the estimated nonlinear effects are established. Extensive simulation studies provide numerical support for the theoretical findings. Data from an HIV study are used to illustrate our methodology.

Article information

Source
Electron. J. Statist., Volume 11, Number 1 (2017), 1549-1569.

Dates
Received: September 2016
First available in Project Euclid: 21 April 2017

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1492740038

Digital Object Identifier
doi:10.1214/17-EJS1266

Mathematical Reviews number (MathSciNet)
MR3638288

Zentralblatt MATH identifier
1362.62066

Subjects
Primary: 62E20: Asymptotic distribution theory
Secondary: 62G05: Estimation

Keywords
Asynchronous longitudinal data estimating estimations local polynomials partially linear models

Rights
Creative Commons Attribution 4.0 International License.

Citation

Chen, Li; Cao, Hongyuan. Analysis of asynchronous longitudinal data with partially linear models. Electron. J. Statist. 11 (2017), no. 1, 1549--1569. doi:10.1214/17-EJS1266. https://projecteuclid.org/euclid.ejs/1492740038


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