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Title: High-dimensional generalized linear model for longitudinal data Authors:  Mohammad Arashi - Ferdowsi University of Mashhad (Iran) [presenting]
Mozhgan Taavoni - Ferdowsi University of Mashhad (Iran)
Abstract: Variable selection and estimation are considered for the additive generalized mixed model with high-dimensional longitudinal data. We use spline approximation for the nonparametric components and use double-penalization for estimation. Under some regularity conditions, the oracle properties of the resulting estimators are established. We assess the performance of the proposed estimation strategy with some numerical analyses.