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Robust estimation of mean and covariance for longitudinal data with dropouts

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  • Guoyou Qin
  • Zhongyi Zhu

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.

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  • Guoyou Qin & Zhongyi Zhu, 2015. "Robust estimation of mean and covariance for longitudinal data with dropouts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1240-1254, June.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1240-1254
    DOI: 10.1080/02664763.2014.999033
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    Cited by:

    1. Guney, Yesim & Arslan, Olcay & Yavuz, Fulya Gokalp, 2022. "Robust estimation in multivariate heteroscedastic regression models with autoregressive covariance structures using EM algorithm," Journal of Multivariate Analysis, Elsevier, vol. 191(C).

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