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• W2084993005 abstract "We propose a penalized quantile regression for partially linear varying coefficient (VC) model with longitudinal data to select relevant non parametric and parametric components simultaneously. Selection consistency and oracle property are established. Furthermore, if linear part and VC part are unknown, we propose a new unified method, which can do three types of selections: separation of varying and constant effects, selection of relevant variables, and it can be carried out conveniently in one step. Consistency in the three types of selections and oracle property in estimation are established as well. Simulation studies and real data analysis also confirm our method." @default.
• W2084993005 created "2016-06-24" @default.
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• W2084993005 date "2014-01-08" @default.
• W2084993005 modified "2023-10-16" @default.
• W2084993005 title "Variable Selection in Semiparametric Quantile Modeling for Longitudinal Data" @default.
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• W2084993005 doi "https://doi.org/10.1080/03610926.2013.857418" @default.
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