Classical and Bayesian Inference for the Inverse Lomax Distribution Under Adaptive Progressive Type-II Censored Data with COVID-19 Application
Journal of Reliability and Statistical Studies
; 15(2):505-534, 2022.
Article
in English
| Scopus | ID: covidwho-1994537
ABSTRACT
In this paper, we consider the classical and the Bayesian inferences for unknown parameters of inverse Lomax distribution and their corresponding survival characteristics under the adaptive progressive type-II censoring scheme. In the classical setup, first we obtain the maximum likelihood estimates for the unknown shape parameter of the distribution and its corresponding survival characteristics. Further, we consider symmetric and asymmetric loss functions for the estimation of shape parameter and its corresponding survival characteristics under the Bayesian paradigm. The performances of various derived estimators were recorded using Markov chain Monte Carlo simulation technique for different sample sizes. Finally,a COVID-19 mortality data set is provided to illustrate the computation of various estimators. © 2022 River Publishers.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Journal of Reliability and Statistical Studies
Year:
2022
Document Type:
Article
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