ANOVA for longitudinal data with missing values
The Annals of Statistics · 2010 · Volume 38 · Issue 6
Abstract
We carry out ANOVA comparisons of multiple treatments for longitudinal studies with missing values. The treatment effects are modeled semiparametrically via a partially linear regression which is flexible in quantifying the time effects of treatments. The empirical likelihood is employed to formulate model-robust nonparametric ANOVA tests for treatment effects with respect to covariates, the nonparametric time-effect functions and interactions between covariates and time. The proposed tests can be readily modified for a variety of data and model combinations, that encompasses parametric, semiparametric and nonparametric regression models; cross-sectional and longitudinal data, and with or without missing values.
Keywords
62G09, 62G10, 62G20, Analysis of variance, empirical likelihood, kernel smoothing, missing at random, Semiparametric model, treatment effects
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