Confidence Regions for Parameters of Negative Binomial Distribution
Emmanuel Nkingi, Jan Vrbik
TL;DR
This paper presents a general method for constructing confidence regions for the two parameters of the Negative Binomial Distribution by expanding the sampling distribution of estimators using the Central Limit Theorem.
Contribution
It introduces a novel approach to derive confidence regions for Negative Binomial parameters based on Method-of-Moments estimators and asymptotic expansion.
Findings
Effective confidence regions can be constructed for Negative Binomial parameters.
The method leverages the Central Limit Theorem for asymptotic approximation.
Provides a general framework applicable to similar distribution parameter estimation.
Abstract
We describe a general method for the construction of a confidence region for the two parameters of the Negative Binomial Distribution. This is achieved by expanding the sampling distribution of Method-of-Moments estimators, using the Central Limit Theorem.
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Taxonomy
TopicsStatistical Distribution Estimation and Applications · Statistical Methods and Bayesian Inference · Advanced Statistical Methods and Models