Robust sparse functional regression model. Issue 9 (27th September 2022)

• Record Type: Journal Article
• Authors: Pannu, Jasdeep; Billor, Nedret
• Is Part Of: Communications in statistics. Volume 51:Issue 9(2022)
• Page Start: 4883
• Page End: 4903
• Publication Date: 2022-09-27
• Subjects: Functional outliers -- Functional regression model -- LASSO -- Least absolute deviation -- Robustness -- Variable selection; 62 Statistics; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2020.1767784
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: The presence of outliers, in general, affects the performance of the conventional statistical methods which require the homogeneity of observations. In this study, we consider variable selection problem in a functional regression model when a functional dataset contains outliers. We propose a functional adaptive group LASSO variable selection method based on the weighted least absolute deviation which takes into account the effect of outliers in both x and y directions for a functional regression model with a scalar response and multiple functional predictors. Further, we demonstrate, through simulated and real datasets, that the proposed methods perform well.