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- W4313436034 abstract "We present a new parsimonious method for joint mean-covariance modeling based on M-estimation and nonconcave penalty. In this paper, the robustness of the proposed model was aimed at addressing the issue when the working matrix is misspecified and a spot of outliers exist in the dataset. The proposed approach outperforms the traditional method in robustness and variable selections for longitudinal data analysis, particularly when the dataset contains a spot of outliers. The simulation results back up the theoretical findings, and the methodology is further illustrated via an analysis of a real progesterone data example." @default.
- W4313436034 created "2023-01-06" @default.
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- W4313436034 date "2022-12-20" @default.
- W4313436034 modified "2023-09-30" @default.
- W4313436034 title "Robust variable selection via nonconcave penalties with an upgraded parsimonious dynamic covariance modeling" @default.
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- W4313436034 doi "https://doi.org/10.1080/03610918.2022.2155308" @default.
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