Outlier detection in statistical modeling via multivariate adaptive regression splines

Murat N.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, no.7, pp.3379-3390, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2023
  • Doi Number: 10.1080/03610918.2021.2007400
  • Journal Name: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.3379-3390
  • Keywords: Multivariate adaptive regression splines, outlier detection, variable selection, LINEAR-REGRESSION, VALUES
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

The outlier detection task is of great importance for identifying spoiling observations in regression analysis. In this paper a novel strategy is proposed to determine outliers for regression models by using multivariate adaptive regression splines (MARS). Within this approach, it is seen that MARS is capable of capturing the clear observation set even when the data set contains numerous outliers. The performance of the proposed strategy is validated with simulation studies and three real data set applications. All the weak and strong aspects of the proposed strategy are evaluated with extensive discussions.