Marginal likelihood for a class of Bayesian generalized linear models
- Author
- NANDRAM, Balgobin1 ; HYUNJOONG KIM2
[1] Department of Mathematical Sciences, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280, United States
[2] Department of Statistics, University of Tennessee, 328 Stokely Management Center, Knoxville, TN 37996, United States - Source
Journal of statistical computation and simulation (Print). 2002, Vol 72, Num 4, pp 319-340, 22 p ; ref : 32 ref
- CODEN
- JSCSAJ
- ISSN
- 0094-9655
- Scientific domain
- Control theory, operational research; Mathematics
- Publisher
- Taylor and Francis, Abingdon
- Publication country
- United Kingdom
- Document type
- Article
- Language
- English
- Keyword (fr)
- Algorithme Metropolis Hastings Analyse donnée Approximation Chaîne Markov Distribution statistique Echantillonnage importance Echantillonnage Estimation Bayes Estimation densité Fonction vraisemblance Implémentation Loi Poisson Loi a priori Loi marginale Maximum vraisemblance Modèle linéaire généralisé Modèle linéaire Modèle régression Mortalité Méthode Monte Carlo Méthode approchée Méthode calcul Méthode paramétrique Problème sélection Rapport vraisemblance Régression statistique Simulation statistique Statistique rang Test ajustement Théorie approximation Transformation Laplace 62-07 62E17 62F07 62F15 62J12 62Jxx Estimation paramétrique Facteur Bayes Méthode sélection Sélection modèle Vraisemblance marginale
- Keyword (en)
- Metropolis Hastings algorithm Data analysis Approximation Markov chain Statistical distribution Importance sampling Sampling Bayes estimation Density estimation Likelihood function Implementation Poisson distribution Prior distribution Marginal distribution Maximum likelihood Generalized linear model Linear model Regression model Mortality Monte Carlo method Approximate method Computing method Parametric method Selection problem Likelihood ratio Statistical regression Statistical simulation Rank statistic Goodness of fit test Approximation theory Laplace transformation Bayes factor Selection method Model selection Marginal likelihood
- Keyword (es)
- Algoritmo Metropolis Hastings Análisis datos Aproximación Cadena Markov Distribución estadística Muestreo Estimación Bayes Estimación densidad Función verosimilitud Ejecución Ley Poisson Ley a priori Ley marginal Maxima verosimilitud Modelo lineal generalizado Modelo lineal Modelo regresión Mortalidad Método Monte Carlo Método aproximado Método cálculo Método paramétrico Problema selección Relación verosimilitud Regresión estadística Simulación estadística Estadística rango Prueba ajuste Transformación Laplace
- Classification
- Pascal
- 001 Exact sciences and technology / 001A Sciences and techniques of general use / 001A02 Mathematics / 001A02H Probability and statistics / 001A02H02 Statistics / 001A02H02F Distribution theory
- Pascal
- 001 Exact sciences and technology / 001A Sciences and techniques of general use / 001A02 Mathematics / 001A02H Probability and statistics / 001A02H02 Statistics / 001A02H02G Parametric inference
- Pascal
- 001 Exact sciences and technology / 001A Sciences and techniques of general use / 001A02 Mathematics / 001A02H Probability and statistics / 001A02H02 Statistics / 001A02H02I Multivariate analysis
- Pascal
- 001 Exact sciences and technology / 001A Sciences and techniques of general use / 001A02 Mathematics / 001A02H Probability and statistics / 001A02H02 Statistics / 001A02H02J Linear inference, regression
- Discipline
- Mathematics
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 13790376
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1080/00949650212842 
https://dx.doi.org/10.1080/00949650212842
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