%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