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

被引：3

作者：

Ozkale, M. Revan ^{[1
]}

机构：

[1] Cukurova Univ, Fac Sci & Letters, Dept Stat, TR-01330 Adana, Turkey

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2021年 / 50卷 / 03期

关键词：

Binary logistic regression; regression diagnostics; principal component logistic estimator; Monte Carlo simulation; Akaike's information criterion; DIAGNOSTICS;

D O I：

10.1080/03610926.2019.1639749

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：609 / 630

页数：22

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