Robust estimation of mean and covariance for longitudinal data with dropouts

Guoyou Qin and Zhongyi Zhu

Journal of Applied Statistics, 2015, vol. 42, issue 6, 1240-1254

Abstract: In this paper, we study estimation of linear models in the framework of longitudinal data with dropouts. Under the assumptions that random errors follow an elliptical distribution and all the subjects share the same within-subject covariance matrix which does not depend on covariates, we develop a robust method for simultaneous estimation of mean and covariance. The proposed method is robust against outliers, and does not require to model the covariance and missing data process. Theoretical properties of the proposed estimator are established and simulation studies show its good performance. In the end, the proposed method is applied to a real data analysis for illustration.

Date: 2015
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DOI: 10.1080/02664763.2014.999033

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