Classical and Bayesian Inference for the Inverse Lomax Distribution Under Adaptive Progressive Type-II Censored Data with COVID-19

  • Rashi Hora Department of Mathematics and Statistics, Banasthali Vidyapith, Tonk, Rajasthan, 304022, India
  • Naresh Chandra Kabdwal Department of Mathematics and Statistics, Banasthali Vidyapith, Tonk, Rajasthan, 304022, India
  • Pulkit Srivastava Department of Statistics, University of Delhi, Delhi, 110007, India
Keywords: Inverse Lomax distribution, adaptive progressive type-II censoring, Maximum likelihood estimator, Bayesian estimation, Markov chain Monte Carlo, COVID-19

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.

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Author Biographies

Rashi Hora, Department of Mathematics and Statistics, Banasthali Vidyapith, Tonk, Rajasthan, 304022, India

Rashi Hora is a research scholar in the department of mathematics and statistics, Banasthali Vidyapith, Rajasthan. Her research area includes survival theory and Bayesian inference. She has the knowledge of many software and languages like R software, OpenBugs and Mathematica.

Naresh Chandra Kabdwal, Department of Mathematics and Statistics, Banasthali Vidyapith, Tonk, Rajasthan, 304022, India

Naresh Chandra Kabdwal, assistant professor of statistics in the department of mathematics and statistics, Banasthali Vidyapith, Rajasthan. He is having 12 years research experience in various fields of statistics such as sequential analysis, survival theory and Bayesian inference.

Pulkit Srivastava, Department of Statistics, University of Delhi, Delhi, 110007, India

Pulkit Srivastava is currently pursuing his Ph.D. at the Department of Statistics, University of Delhi, Delhi. His research areas include Bayesian Inference, Stochastic processes etc.

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Published
2022-08-02
Section
Articles