The Annals of Statistics

Logistic Regression Diagnostics

Daryl Pregibon

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Abstract

A maximum likelihood fit of a logistic regression model (and other similar models) is extremely sensitive to outlying responses and extreme points in the design space. We develop diagnostic measures to aid the analyst in detecting such observations and in quantifying their effect on various aspects of the maximum likelihood fit. The elements of the fitting process which constitute the usual output (parameter estimates, standard errors, residuals, etc.) will be used for this purpose. With a properly designed computing package for fitting the usual maximum-likelihood model, the diagnostics are essentially "free for the asking." In particular, good data analysis for logistic regression models need not be expensive or time-consuming.

Article information

Source
Ann. Statist. Volume 9, Number 4 (1981), 705-724.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
http://projecteuclid.org/euclid.aos/1176345513

JSTOR
links.jstor.org

Digital Object Identifier
doi:10.1214/aos/1176345513

Mathematical Reviews number (MathSciNet)
MR619277

Zentralblatt MATH identifier
0478.62053

Subjects
Primary: 62F99: None of the above, but in this section
Secondary: 62J05: Linear regression 62P10: Applications to biology and medical sciences

Keywords
Logistic regression generalized linear models regression diagnostics residual analysis iteratively reweighted least squares leverage points influence curves

Citation

Pregibon, Daryl. Logistic Regression Diagnostics. Ann. Statist. 9 (1981), no. 4, 705--724. doi:10.1214/aos/1176345513. http://projecteuclid.org/euclid.aos/1176345513.


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