A new correction approach for information criteria to detect outliers in regression modeling

Dünder E.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, cilt.50, sa.10, ss.2451-2465, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 10
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/03610926.2020.1792497
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2451-2465
  • Anahtar Kelimeler: Corrected information criteria, lasso, regression modeling, outlier detection, ROBUST REGRESSION, SELECTION, ALGORITHMS
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

The outliers cause wrong prediction and estimation results in regression models. Therefore, it is important to identify the outliers correctly in the context of regression analysis. Information criteria can be used to perform this task with corrections but these corrected versions of criteria require some subjective parameters. In this article, an objective correction approach is proposed within the information criteria to perform outlier detection in regression modeling. The evaluations are performed on lasso regression. The numerical examples demonstrate that the proposed correction successfully achieves the outlier detection task in regression models without requiring any subjective correction parameter.