On Hybrid Censored Inverse Lomax Distribution: Application to the Survival Data

Abhimanyu Singh Yadav (), Sanjay Kumar Singh and Umesh Singh
Additional contact information
Abhimanyu Singh Yadav: Mizoram University, Aizawl - India
Sanjay Kumar Singh: Banaras Hindu University, Varanasi - India
Umesh Singh: Banaras Hindu University, Varanasi - India

Statistica, 2016, vol. 76, issue 2, 185-203

Abstract: In this paper, we proposed the estimation procedures to estimate the unknown parameters, reliability and hazard functions of Inverse Lomax distribution. The mathematical expressions for maximum likelihood and Bayes estimators are derived in presence of hybrid censoring scheme. In most of the cases, it has been seen that maximum likelihood and Bayes estimators of the parameters are not appear in explicit form. Hence, Newton-Raphson (N-R) method has been used to draw the maximum likelihood estimates of the parameters. The Bayes estimators are obtained under Jeffrey's non-informative prior for both shape and scale using Markov Chain Monte Carlo (MCMC) technique. Further, we have also constructed the 95% asymptotic confidence interval based on maximum likelihood estimates (MLEs) and highest posterior density (HPD) credible intervals based on MCMC samples. Finally, two data sets have been used to demonstrate the proposed methodology.

Keywords: Parameters estimation; hybrid censoring; MCMC method (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (5)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:76:y:2016:i:2:p:185-203

Access Statistics for this article

Statistica is currently edited by Department of Statistics, University of Bologna

More articles in Statistica from Department of Statistics, University of Bologna Contact information at EDIRC.
Bibliographic data for series maintained by Giovanna Galatà ().