Treffer: One- and Two-Sample Bayesian Prediction Intervals Based on Type-I Hybrid Censored Data
Title:
One- and Two-Sample Bayesian Prediction Intervals Based on Type-I Hybrid Censored Data
Authors:
Source:
Communications in statistics. Simulation and computation. 41(1-2):65-88
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Théorie des probabilités et processus stochastiques, Probability theory and stochastic processes, Lois de probabilités, Distribution theory, Processus de markov, Markov processes, Statistiques, Statistics, Inférence non paramétrique, Nonparametric inference, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse numérique, Numerical analysis, Análisis numérico, Chaîne Markov, Markov chain, Cadena Markov, Distribution statistique, Statistical distribution, Distribución estadística, Donnée censurée, Censored data, Donnée observation, Observation data, Dato observación, Durée vie, Lifetime, Tiempo vida, Echantillon censuré, Censored sample, Muestra censurada, Echantillonnage Gibbs, Gibbs sampling, Muestreo Gibbs, Estimation Bayes, Bayes estimation, Estimación Bayes, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Estimation statistique, Statistical estimation, Estimación estadística, Fonction répartition, Distribution function, Función distribución, Fonction survie, Survival function, Función sobrevivencia, Loi Pareto, Pareto distribution, Ley Pareto, Loi a priori, Prior distribution, Ley a priori, Loi exponentielle, Exponential distribution, Ley exponencial, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Méthode stochastique, Stochastic method, Método estocástico, Processus stochastique, Stochastic process, Proceso estocástico, Prédiction, Prediction, Predicción, Simulation numérique, Numerical simulation, Simulación numérica, Statistique ordre, Order statistic, Estadística orden, Théorie approximation, Approximation theory, Théorie filtrage, Filtering theory, Théorie prédiction, Prediction theory, 60E05, 60G25, 60J10, 62E17, 62G30, 62M20, 62N01, 65C05, 65C40, Fonction répartition empirique, Intervalle prédiction, Prediction interval, Bayesian prediction, Markov Chain Monte Carlo, Order statistics, Primary 62G30, Secondary 62F15, Type-I hybrid censored sample
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics, Fayoum University, Fayoum, Egypt
Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
Faculty of Science, King Saud University, Riyadh, Saudi Arabia
Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
Faculty of Science, King Saud University, Riyadh, Saudi Arabia
ISSN:
0361-0918
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
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
CC BY 4.0
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
Notes:
Mathematics
Accession Number:
edscal.25576912
Database:
PASCAL Archive
Weitere Informationen
In this article, we consider a general form for the underlying distribution and a general conjugate prior, and describe a general procedure for determining the Bayesian prediction intervals for future lifetimes based on an observed Type-I hybrid censored data. For the illustration of the developed results, the Exponential(0) and Pareto(α, β) distributions are used as examples. One-sample Bayesian predictive survival function can not be obtained in closed-form and so Gibbs sampling procedure is used to draw Markov Chain Monte Carlo (MCMC) samples, which are then used to compute the approximate predictive survival function. Finally, some numerical results are presented to illustrate all the inferential results developed here.