Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry
Abstract
A procedure for asymptotic bias reduction of maximum likelihood estimates of generic estimands is developed. The estimator is realized as a plug-in estimator, where the parameter maximizes the penalized likelihood with a penalty function that satisfies a quasi-linear partial differential equation of the first order. The integration of the partial differential equation with the aid of differential geometry is discussed. Applications to generalized linear models, linear mixed-effects models, and a location-scale family are presented.
Fields of Study & Subjects
Statistical Methods and Inference
Publication details
- Journal / source
- Bernoulli
- Volume
- 32
- Issue
- 3
- Publication date
- 29 April 2026
- Language
- EN