Estimating the logistic regression equation when the model is incorrect
Abstract
Protesting mildly against the notion of an exactly correct parametric model the view is adopted that the logistic regression equation is merely an approximation to the underlying, true function. The behaviour of likelihood based estimators is investigated in such a general framework. The maximum likelihood estimator is shown to be consistent for a certain least false parameter value minimising a weighted average of quantities that measure the distance from the true to the parametric model. Asymptotic normality is also demonstrated. Finally a number of additional remarks are offered, some pointing to natural generalisations and some to new questions for research, like weighted and local likelihood estimation methods.
Cite
@article{arxiv.2605.26753,
title = {Estimating the logistic regression equation when the model is incorrect},
author = {Nils Lid Hjort},
journal= {arXiv preprint arXiv:2605.26753},
year = {2026}
}
Comments
8 pages, 0 figures. Statistical Research Report, Department of Mathematics, University of Oslo, January 1990; the material foreshadows later developments regarding local likelihood (for regression and for densities), weighted likelihood, robust inference, and more