We propose a likelihood function endowed with a penalization that reduces the bias of the maximum likelihood estimator in parametric models. The penalization hinges on the first two derivatives of the log likelihood and can be computed numerically. The behavior and the sensitivity to nuisance parameters of the penalized likelihood and derived quantities are addressed. An illustration is provided in survival models for stratified censored data.
On penalized likelihood and bias reduction
LUNARDON, NICOLA;ADIMARI, GIANFRANCO
2016
Abstract
We propose a likelihood function endowed with a penalization that reduces the bias of the maximum likelihood estimator in parametric models. The penalization hinges on the first two derivatives of the log likelihood and can be computed numerically. The behavior and the sensitivity to nuisance parameters of the penalized likelihood and derived quantities are addressed. An illustration is provided in survival models for stratified censored data.
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