Identification of outlying and influential data with principal components regression estimation in binary logistic regression

Ozkale M. R.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.50, no.3, pp.609-630, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 3
  • Publication Date: 2021
  • Doi Number: 10.1080/03610926.2019.1639749
  • Journal Indexes: 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
  • Page Numbers: pp.609-630
  • Keywords: Binary logistic regression, regression diagnostics, principal component logistic estimator, Monte Carlo simulation, Akaike's information criterion, DIAGNOSTICS
  • Çukurova University Affiliated: Yes


In this study, we settle on the issue that when multicollinearity and unusual observations arise simultaneously and we straightforwardly extend leverages, Pearson residuals, delta beta and delta chi-square statistics using the principal components logistic regression (PCLR) estimator where the extensions typically take the advantage of the computation of PCLR estimator by one-step approximation. We then applied two simulation studies and a numerical example to illustrate the behavior of statistics for the PCLR estimator versus the traditional ML estimator.