%0 Journal Article
%T Statistical models: Conventional, penalized and hierarchical likelihood
%+ Epidémiologie et Biostatistique [Bordeaux]
%A Commenges, Daniel
%< avec comité de lecture
%@ 1935-7516
%J Statistics Surveys
%I Institute of Mathematical Statistics (IMS)
%V 3
%P 1-17
%8 2009
%D 2009
%R 10.1214/08-SS039
%K Bayes estimators
%K cross-validation
%K h-likelihood
%K incomplete data
%K Kullback-Leibler risk
%K sieves
%K statistical models.
%K likelihood
%K penalized likelihood
%K statistical models
%Z Life Sciences [q-bio]/Santé publique et épidémiologieJournal articles
%X We give an overview of statistical models and likelihood, together with two of its variants: penalized and hierarchical likelihood. The Kullback-Leibler divergence is referred to repeatedly in the literature, for defining the misspecification risk of a model and for grounding the likelihood and the likelihood cross-validation, which can be used for choosing weights in penalized likelihood. Families of penalized likelihood and particular sieves estimators are shown to be equivalent. The similarity of these likelihoods with a posteriori distributions in a Bayesian approach is considered
%G English
%2 https://inserm.hal.science/inserm-00373280/document
%2 https://inserm.hal.science/inserm-00373280/file/Commenges_1.pdf
%L inserm-00373280
%U https://inserm.hal.science/inserm-00373280
%~ INSERM
%~ SANTE_PUB_INSERM