Download This PaperHypothesis Testing of Poisson Rates in COVID-19 Offspring Distributions
13 Pages Posted: 17 Jan 2023
Date Written: January 3, 2023
Abstract
In this study, we deal with the hypothesis testing problem of Poisson distributed data. The purpose of the test is to determine whether two collections of discrete data have the same Poisson rate. We review and compare several frequientist and Bayesian methods that deal with the problem, such as the conditional test, the likelihood ratio test, and the Bayes factor. We also utilize the posterior predictive p-value and its associated calibration procedures. Finally, we apply the various methods in testing simulated datasets as well as the offspring distributions associated with COVID-19 cases in Hong Kong and Rwanda.
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Funding Declaration: none
Conflict of Interests: The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
Keywords: Poisson distribution, hypothesis testing, Bayes factor, posterior predictive p-value, COVID-19
JEL Classification: C12
Suggested Citation: Suggested Citation