A useful approach to identify the multicollinearity in the presence of outliers

Sinan A., Alkan B. B.

JOURNAL OF APPLIED STATISTICS, cilt.42, sa.5, ss.986-993, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 5
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1080/02664763.2014.993369
  • Dergi Adı: JOURNAL OF APPLIED STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.986-993
  • Anahtar Kelimeler: minimum covariance determinant, outliers, multicollinearity, COVARIANCE DETERMINANT ESTIMATOR, PRINCIPAL COMPONENT ANALYSIS, ROBUST, REGRESSION
  • Akdeniz Üniversitesi Adresli: Hayır

Özet

The presence of outliers in the data sets affects the structure of multicollinearity which arises from a high degree of correlation between explanatory variables in a linear regression analysis. This affect could be seen as an increase or decrease in the diagnostics used to determine multicollinearity. Thus, the cases of outliers reduce the reliability of diagnostics such as variance inflation factors, condition numbers and variance decomposition proportions. In this study, we propose to use a robust estimation of the correlation matrix obtained by the minimum covariance determinant method to determine the diagnostics of multicollinearity in the presence of outliers. As a result, the present paper demonstrates that the diagnostics of multicollinearity obtained by the robust estimation of the correlation matrix are more reliable in the presence of outliers.