Group LASSO for high-dimensional partially linear errors-invariables models
Publish place: 2rd International Conference on Soft Computing
Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CSCG02_078
تاریخ نمایه سازی: 7 اسفند 1396
Abstract:
This article focuses on group variable selection for high dimensional partially linear models when the covariates are measured with additive errors. We apply the group least absolute and shrinkage operator (LASSO) penalty to simultaneously estimate and select significant variables. Finite sample performance of the proposed procedure is assessed by simulation studies, where we compare the naive and bias-corrected group LASSO estimators
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Authors
m kazemi
Department of Statistics, Shahrood University of Technology, Shahrood, Iran
d shahsavani
Department of Statistics, Shahrood University of Technology, Shahrood, Iran
m arashi
Department of Statistics, Shahrood University of Technology, Shahrood, Iran