Skip to Main content Skip to Navigation
Journal articles

Statistical models: Conventional, penalized and hierarchical likelihood

Abstract : 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
Document type :
Journal articles
Complete list of metadatas

Cited literature [43 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-00373280
Contributor : Evelyne Mouillet <>
Submitted on : Friday, April 3, 2009 - 4:30:41 PM
Last modification on : Wednesday, November 29, 2017 - 2:54:36 PM
Long-term archiving on: : Thursday, June 10, 2010 - 7:43:09 PM

File

Commenges_1.pdf
Publisher files allowed on an open archive

Identifiers

Collections

Citation

Daniel Commenges. Statistical models: Conventional, penalized and hierarchical likelihood. Statistics Surveys, Institute of Mathematical Statistics (IMS), 2009, 3, pp.1-17. ⟨10.1214/08-SS039⟩. ⟨inserm-00373280⟩

Share

Metrics

Record views

207

Files downloads

383