%0 Journal Article
%T Statistical models: Conventional, penalized and hierarchical likelihood
%A Daniel Commenges
%J Statistics Surveys
%D 2009
%I Statistics Surveys
%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.
%K Bayes estimators
%K Cross-validation
%K h-likelihood
%K Incomplete data
%K Kullback-Leibler risk
%K Likelihood
%K Penalized likelihood
%K Sieves
%K Statistical models
%U http://projecteuclid.org/euclid.ssu/1239113309