چکیده مقاله
This article is concerned with feature selection for varying coefficient models with ultrahighdimensional covariates We propose a two stage approach for these models The two stage approach consists of a reducing the ultrahigh dimensionality by using a new feature screening procedure based on partial correlation coefficient and b applying regularization methods for dimension reduced varying coefficient models to further select important variables and estimate the coefficient functions We illustrate the proposed two stage approach by simulation study and a real data example
کلیدواژهها
feature selection screening Big data high-dimensional data rank regression varying coefficient model high dimensional data
نویسندگان
Mohammad Kazemi
Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran;
شیوه ارجاع
Kazemi, Mohammad,1398,Feature Selection for Ultrahigh Dimensional Varying Coefficient Models,3rd International Conference on Soft Computing,Rudsar
ارائهشده در
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