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Model Detection and Variable Selection for Varying Coefficient Models with Longitudinal Data 被引量:1
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作者 San Ying FENG Yu Ping HU Liu Gen XUE 《Acta Mathematica Sinica,English Series》 SCIE CSCD 2016年第3期331-350,共20页
In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, d... In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficients simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and the separation of varying and constant coefficients, and the penalized estimators have the oracle property. Finite sample performances of the proposed method are illustrated by some simulation studies and the real data analysis. 展开更多
关键词 combined penalization longitudinal data model detection variable selection oracle property varying coefficient model
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