This article introduces adaptive weighted maximum likelihood estimators for binary regression models. The asymptotic distribution under the model is established, and asymptotic confidence intervals are derived. Finite-sample properties are studied by simulation. For clean datasets, the proposed adaptive estimators are more efficient than the non-adaptive ones even for moderate sample sizes, and for outlier-contaminated datasets they show a comparable robustness. As for the asymptotic confidence intervals, the actual coverage levels under the model are very close to the nominal levels (even for moderate sample sizes), and they are reasonably stable under contamination. © 2004 Elsevier B.V. All rights reserved.
Gervini, D. (2005). Robust adaptive estimators for binary regression models. Journal of Statistical Planning and Inference, 131(2), 297–311. https://doi.org/10.1016/j.jspi.2004.02.006