Electronic Journal of Statistics

Analysis of asynchronous longitudinal data with partially linear models

Li Chen and Hongyuan Cao

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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

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

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

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

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

Asynchronous longitudinal data estimating estimations local polynomials partially linear models

Creative Commons Attribution 4.0 International License.


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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