Outlier detection in statistical modeling via multivariate adaptive regression splines

Murat N.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, sa.7, ss.3379-3390, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1080/03610918.2021.2007400
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: 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
  • Sayfa Sayıları: ss.3379-3390
  • Anahtar Kelimeler: Multivariate adaptive regression splines, outlier detection, variable selection, LINEAR-REGRESSION, VALUES
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

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.