FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2024159897> ?p ?o ?g. }

• W2024159897 endingPage "250" @default.
• W2024159897 startingPage "239" @default.
• W2024159897 abstract "A multi-level model allows the possibility of marginalization across levels in different ways, yielding more than one possible marginal likelihood. Since log-likelihoods are often used in classical model comparison, the question to ask is which likelihood should be chosen for a given model. The authors employ a Bayesian framework to shed some light on qualitative comparison of the likelihoods associated with a given model. They connect these results to related issues of the effective number of parameters, penalty function, and consistent definition of a likelihood-based model choice criterion. In particular, with a two-stage model they show that, very generally, regardless of hyperprior specification or how much data is collected or what the realized values are, a priori, the first-stage likelihood is expected to be smaller than the marginal likelihood. A posteriori, these expectations are reversed and the disparities worsen with increasing sample size and with increasing number of model levels. Dans un modèle multi-couches, il y a plusieurs façons de procéder à une marginalisation, et donc plus d'une vraisemblance marginale. Sachant que les log-vraisemblances sont souvent employées pour comparer des modèles entre eux, on se demande quelle vraisemblance choisir pour un modèle particulier. Adoptant une approche bayésienne, les auteurs jettent un éclairage nouveau sur la comparaison qualitative des diverses vraisemblances associées à un modèle donné. Ils lient ces résultats à des questions périphéri-ques concernant le nombre de paramètres, la pénalisation et la définition d'un critère de choix de modèle cohérent avec la vraisemblance. Dans le cadre particulier d'un modèle à deux couches, ils montrent que sous des conditions très générales, l'espérance de la vraisemblance de premier niveau est, a priori, plus petite que celle de la vraisemblance marginale, et ce peu importe le choix de la loi a priori sur les hyper-paramètres, le nombre ou la valeur des observations recueillies. A posteriori, l'inégalité est renversée et l'écart se creuse à mesure qu'augmentent la taille échantillonnale et le nombre de couches du modèle." @default.
• W2024159897 created "2016-06-24" @default.
• W2024159897 creator A5023384550 @default.
• W2024159897 creator A5042522667 @default.
• W2024159897 date "2003-09-01" @default.
• W2024159897 modified "2023-10-03" @default.
• W2024159897 title "Inequalities between expected marginal log-likelihoods, with implications for likelihood-based model complexity and comparison measures" @default.
• W2024159897 cites W1569625389 @default.
• W2024159897 cites W1967396577 @default.
• W2024159897 cites W2057765075 @default.
• W2024159897 cites W2073421296 @default.
• W2024159897 cites W2101122849 @default.
• W2024159897 cites W2127874106 @default.
• W2024159897 cites W2168175751 @default.
• W2024159897 cites W2403035479 @default.
• W2024159897 cites W2502637120 @default.
• W2024159897 cites W4206653471 @default.
• W2024159897 cites W4211177544 @default.
• W2024159897 doi "https://doi.org/10.2307/3316084" @default.
• W2024159897 hasPublicationYear "2003" @default.
• W2024159897 type Work @default.
• W2024159897 sameAs 2024159897 @default.
• W2024159897 citedByCount "14" @default.
• W2024159897 countsByYear W20241598972013 @default.
• W2024159897 countsByYear W20241598972014 @default.
• W2024159897 countsByYear W20241598972017 @default.
• W2024159897 countsByYear W20241598972018 @default.
• W2024159897 countsByYear W20241598972019 @default.
• W2024159897 countsByYear W20241598972020 @default.
• W2024159897 crossrefType "journal-article" @default.
• W2024159897 hasAuthorship W2024159897A5023384550 @default.
• W2024159897 hasAuthorship W2024159897A5042522667 @default.
• W2024159897 hasConcept C105795698 @default.
• W2024159897 hasConcept C107673813 @default.
• W2024159897 hasConcept C114614502 @default.
• W2024159897 hasConcept C142291917 @default.
• W2024159897 hasConcept C149782125 @default.
• W2024159897 hasConcept C160234255 @default.
• W2024159897 hasConcept C33923547 @default.
• W2024159897 hasConcept C49781872 @default.
• W2024159897 hasConcept C89106044 @default.
• W2024159897 hasConcept C9483764 @default.
• W2024159897 hasConcept C95923904 @default.
• W2024159897 hasConceptScore W2024159897C105795698 @default.
• W2024159897 hasConceptScore W2024159897C107673813 @default.
• W2024159897 hasConceptScore W2024159897C114614502 @default.
• W2024159897 hasConceptScore W2024159897C142291917 @default.
• W2024159897 hasConceptScore W2024159897C149782125 @default.
• W2024159897 hasConceptScore W2024159897C160234255 @default.
• W2024159897 hasConceptScore W2024159897C33923547 @default.
• W2024159897 hasConceptScore W2024159897C49781872 @default.
• W2024159897 hasConceptScore W2024159897C89106044 @default.
• W2024159897 hasConceptScore W2024159897C9483764 @default.
• W2024159897 hasConceptScore W2024159897C95923904 @default.
• W2024159897 hasIssue "3" @default.
• W2024159897 hasLocation W20241598971 @default.
• W2024159897 hasOpenAccess W2024159897 @default.
• W2024159897 hasPrimaryLocation W20241598971 @default.
• W2024159897 hasRelatedWork W1572105898 @default.
• W2024159897 hasRelatedWork W1875078516 @default.
• W2024159897 hasRelatedWork W2048955895 @default.
• W2024159897 hasRelatedWork W2141324124 @default.
• W2024159897 hasRelatedWork W2166748222 @default.
• W2024159897 hasRelatedWork W2405192631 @default.
• W2024159897 hasRelatedWork W2473791430 @default.
• W2024159897 hasRelatedWork W3141775592 @default.
• W2024159897 hasRelatedWork W4230243315 @default.
• W2024159897 hasRelatedWork W4233488282 @default.
• W2024159897 hasVolume "31" @default.
• W2024159897 isParatext "false" @default.
• W2024159897 isRetracted "false" @default.
• W2024159897 magId "2024159897" @default.
• W2024159897 workType "article" @default.