典型文献
Robust Error Density Estimation in Ultrahigh Dimensional Sparse Linear Model
文献摘要：
This paper focuses on error density estimation in ultrahigh dimensional sparse linear model,where the error term may have a heavy-tailed distribution.First,an improved two-stage refitted cross-validation method combined with some robust variable screening procedures such as RRCS and variable selection methods such as LAD-SCAD is used to obtain the submodel,and then the residual-based kernel density method is applied to estimate the error density through LAD regression.Under given conditions,the large sample properties of the estimator are also established.Especially,we explicitly give the relationship between the sparsity and the convergence rate of the kernel density estimator.The simulation results show that the proposed error density estimator has a good performance.A real data example is presented to illustrate our methods.
文献关键词：
中图分类号：
作者姓名：
Feng ZOU;Heng Jian CUI
作者机构：
School of Mathematical Sciences,Capital Normal University,Beijing 100048,P.R.China
文献出处：
引用格式：
[1]Feng ZOU;Heng Jian CUI-.Robust Error Density Estimation in Ultrahigh Dimensional Sparse Linear Model)[J].数学学报（英文版）,2022(06):963-984
A类：
refitted,RRCS,submodel
B类：
Robust,Error,Density,Estimation,Ultrahigh,Dimensional,Sparse,Linear,Model,This,paper,focuses,error,density,estimation,ultrahigh,dimensional,sparse,linear,where,term,may,have,heavy,tailed,distribution,First,improved,two,stage,cross,validation,combined,some,robust,variable,screening,procedures,such,selection,methods,LAD,SCAD,used,obtain,then,residual,kernel,applied,estimate,through,regression,Under,given,conditions,large,sample,properties,estimator,are,also,established,Especially,explicitly,relationship,between,sparsity,convergence,simulation,results,show,that,proposed,has,good,performance,real,data,example,presented,illustrate,our
AB值：
0.659519
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