Statistical inferences based on exponentiated exponential model to assess novel corona virus (COVID-19) kerala patient data
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Annals of Data Science
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 maximumlikelihood
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.
Description
Keywords
MLE · Bayes estimate · Bayes prediction · LR test · Empirical posterior probabilit