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- W2020311127 abstract "In longitudinal data analysis, a correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this article, we consider robust variable selection method in a joint mean and covariance model. We propose a set of penalized robust generalized estimating equations to simultaneously estimate the mean regression coefficients, the general- ized autoregressive coefficients, and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covari- ate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, we develop the oracle property of the proposed robust variable selection method. Finally, a simulation study and a detailed data analysis are carried out to assess and illustrate the small sample per- formance; they show that the proposed method performs favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model." @default.
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- W2020311127 date "2014-01-01" @default.
- W2020311127 modified "2023-09-27" @default.
- W2020311127 title "Variable selection in robust joint mean and covariance model for longitudinal data analysis" @default.
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- W2020311127 doi "https://doi.org/10.5705/ss.2011.251" @default.
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