Analysis of asynchronous longitudinal data with partially linear models | Zendy

Li Chen | Zendy; Hongyuan Cao | Zendy

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Analysis of asynchronous longitudinal data with partially linear models

Author(s) -

Li Chen,

Hongyuan Cao

Publication year - 2017

Publication title -

electronic journal of statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.482

H-Index - 54

ISSN - 1935-7524

DOI - 10.1214/17-ejs1266

Subject(s) - mathematics , nonparametric statistics , asynchronous communication , nonlinear system , parametric statistics , longitudinal data , linear model , parametric model , semiparametric regression , kernel (algebra) , kernel method , mathematical optimization , econometrics , statistics , computer science , support vector machine , artificial intelligence , data mining , discrete mathematics , computer network , physics , quantum mechanics

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. MSC 2010 subject classifications: Primary 62E20; secondary 62G05.

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