چکیده مقاله
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
کلیدواژهها
Adaptive group LASSO Measurement errors Partially linear model
نویسندگان
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
شیوه ارجاع
kazemi, m and shahsavani, d and arashi, m,1396,Group LASSO for high-dimensional partially linear errors-invariables models,2rd International Conference on Soft Computing,Rudsar
ارائهشده در
مجموعه مقالات دومین کنفرانس ملی محاسبات نرم1 آذر 1396 · رودسر