Classical and Bayesian Estimation for the Parameters of a Competing Risk Model Based on Minimum of Exponential and Gamma Failures

The paper provides both classical and Bayesian estimation of the parameters of a competing risk model defined on the basis of minimum of exponential and gamma failure modes. Usually such situations are the examples of incomplete specification of data that naturally opens the way to expectation maxim...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on reliability 2016-09, Vol.65 (3), p.1522-1535
Main Authors: Ranjan, Rakesh, Upadhyay, S. K.
Format: Article
Language:English
Subjects:
Aging
Bayes methods
Bayesian analysis
Competing risk model
Computer simulation
Data models
Estimation
expectation maximization algorithm
exponential model
gamma model
Hazards
increasing hazard rate
Mathematical models
Maximization
Numerical models
Parameter estimation
Parameters
Reliability
Risk
sampling importance resampling
Specifications
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The paper provides both classical and Bayesian estimation of the parameters of a competing risk model defined on the basis of minimum of exponential and gamma failure modes. Usually such situations are the examples of incomplete specification of data that naturally opens the way to expectation maximization algorithm for obtaining maximum likelihood estimates of model parameters. This incomplete specification of the data simultaneously explores the possibility of sampling importance resampling strategy with intermediate Markov chain Monte Carlo steps for the Bayesian estimation of parameters. Although this paper focuses primarily on estimation of model parameters, other inferential developments can be routinely done. Numerical illustration is provided based on both simulated and real-data examples.
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2016.2575439