Heteroscedastic Weibull-Normal Mixture Models: A Bayesian Approach

DOI10.1080/03610926.2012.659826zbMath1462.62418OpenAlexW2007062910MaRDI QIDQ2815359

Edilberto C. Cuervo, Liliana Garrido

Publication date: 28 June 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2012.659826

zbMATH Keywords

Bayesian inferencemixture modelsnormal regressionWeibull regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15)

Related Items (1)

Bayesian analysis for heteroscedastic normal-Pareto mixture model with application

Cites Work

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• Log-modified Weibull regression models with censored data: sensitivity and residual analysis
• Bayes inference in regression models with ARMA\((p,q)\) errors
• Bayesian analysis of mixture models with an unknown number of components\,--\,an alternative to reversible jump methods.
• Mixture models for type II censored survival data in the presence of covariates
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Modeling menstrual cycle length using a mixture distribution
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Dealing With Label Switching in Mixture Models
• Perfect Samplers for Mixtures of Distributions
• Marginal Likelihood From the Metropolis–Hastings Output
• Mixture of Distributions in the Biparametric Exponential Family: A Bayesian Approach
• Monte Carlo sampling methods using Markov chains and their applications
• Random effects Weibull regression model for occupational lifetime

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