Jeffreys Prior for Negative Binomial and Zero Inflated Negative Binomial Distributions

Arnab Kumar Maity () and Erina Paul ()
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Arnab Kumar Maity: Pfizer Inc.
Erina Paul: Merck, Co., Inc.

Sankhya A: The Indian Journal of Statistics, 2023, vol. 85, issue 1, No 41, 999-1013

Abstract: Abstract The negative binomial distribution often fits many real datasets, for example, RNA sequence data, adequately. Furthermore, in the presence of many zeros in the data, it is customary to fit a zero inflated negative binomial distribution. In this note, we study the effect of assuming the Jeffreys prior on the parameters of these two distributions. Under this, we derive the closed form expression of the Bayes factor of the zero inflated negative binomial against negative binomial distribution. We demonstrate the effectiveness of our findings through simulations and real data analyses.

Keywords: Bayes factor; marginal likelihood; negative binomial distribution; Jeffreys prior; zero inflated negative binomial distribution.; Primary: 62XX; Secondary: 62-08 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13171-022-00286-3

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