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

M. Revan Özkale

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 3, 609-630

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:50:y:2021:i:3:p:609-630

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DOI: 10.1080/03610926.2019.1639749

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