Sparse Semi-Functional Partial Linear Single-Index Regression

Proceedings

Vieu, Philippe¹  Aneiros, Germán²  Novo, Silvia³ 

[1] Author to whom correspondence should be addressed.;Institut de Mathématiques, Université Paul Sabatier, 31062 Toulouse, France;MODES Research Group, CITIC, Universidade da Coruña, 15071 A Coruña, Spain

关键词: functional data analysis;    variable selection;    sparse model;    dimension reduction;    functional single-index model;    semiparametric model;   

DOI  :  10.3390/proceedings2181190

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

The variable selection problem is studied in the sparse semi-functional partial linear model, with single-index type influence of the functional covariate in the response. The penalized least squares procedure is employed for this task. Some properties of the resultant estimators are derived: the existence (and rate of convergence) of a consistent estimator for the parameters in the linear part and an oracle property for the variable selection method. Finally, a real data application illustrates the good performance of our procedure.

【 授权许可】

CC BY