Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data

Anurag Pathak, Manoj Kumar (), Sanjay Kumar Singh and Umesh Singh
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Anurag Pathak: Central University of Haryana
Manoj Kumar: Central University of Haryana
Sanjay Kumar Singh: Banaras Hindu University
Umesh Singh: Banaras Hindu University

Annals of Data Science, 2022, vol. 9, issue 1, No 6, 119 pages

Abstract: Abstract In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the model has been followed by different statistical tools like the value of logarithm of likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian information criterion. Moreover, likelihood ratio test and empirical posterior probability analysis are performed to show its suitability. The maximum-likelihood and asymptotic confidence intervals for the parameters are derived from Fisher information matrix. We use the Markov Chain Monte Carlo technique to generate samples from the posterior density function. Based on generated samples, we can compute the Bayes estimates of the unknown parameters and can also construct highest posterior density credible intervals. Further we discuss the Bayesian prediction for future observation based on the observed sample. The Gibbs sampling technique has been used for estimating the posterior predictive density and also for constructing predictive intervals of the order statistics from the future sample.

Keywords: MLE; Bayes estimate; Bayes prediction; LR test; Empirical posterior probability (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s40745-021-00348-7

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