Robust variable selection and structure identification for varying coefficient models
This paper investigated the problem of variable selection and structure identification in varying coefficient model under robust regression.By using B-spline basis function to approximate the non-parametric part,it proposed a combined penalization procedure to select the significant variables,detect the true structure of the model and estimate the unknown regression coefficients simultaneously.It proved that under certain conditions,the consistency and sparsity of the proposed procedure are valid.In addition,it illustrated finite sample performances of the proposed method through some simulation studies.
varying coefficient modelsrobust regressionadaptive group LassoVariable selectionsparsity
万方数据
维普
