Mixture models for type II censored survival data in the presence of covariates
type: Item
instance of: scholarly article
title: Mixture models for type II censored survival data in the presence of covariates
zbMATH Open document ID: 0934.62100
author: Jorge Alberto Achcar
author: Gilberto De Araujo Pereira
published in: Computational Statistics
publication date: 2000-03-01T00:00:00Z
review text: The authors develop a Bayesian approach to the analysis of mixture models for survival data in presence of type II censoring. The normal-exponential, normal-normal and gamma-normal mixture models are concidered, and Markov chain Monte Carlo (MCMC) methods based on Gibbs sampling with Metropolis-Hastings algorithms are used to estimate the posterior quantities of interest. A numerical example illustrates the proposed methodology and some computational aspects of the MCMC algorithms.
Mathematics Subject Classification ID: 62N01
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 65C40
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 1408665
zbMATH Keywords: mixture models
zbMATH Keywords: Gibbs samples
zbMATH Keywords: Metropolis-Hastings algorithm
MaRDI profile type: Publication
full work available at URL: s001800050015
OpenAlex ID: W2012104126
cites work: Bayesian Analysis of Binary and Polychotomous Response Data
cites work: Q4139463
cites work: Q4298912
cites work: Sampling-Based Approaches to Calculating Marginal Densities
cites work: Inference from iterative simulation using multiple sequences
cites work: Q4427562
cites work: Q4865954
cites work: Q4272782
cites work: The Calculation of Posterior Distributions by Data Augmentation
cites work: Statistical analysis of finite mixture distributions
DOI: 10.1007/S001800050015
description: scientific article; zbMATH DE number 1408665