On Analysis of the Behrens-Fisher Problem Based on Bayesian Evidence

Zaria,; Nigéria,

doi:10.4236/ojs.2019.91001

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…A prior distribution could be informative or non-informative, improper or conjugate. For a demonstration on the use of priors, see Nawaz and Aslam [8], Chandra and Rathavr [9], Aslam et al [10], Guure and Bosomprah [5], Yin [11], Yin and Zhao [12], Yin and Li [13], Goltong and Doguwa [14] and Goltong and Doguwa [15].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of the Parameters of the Odd Generalized Exponentiated - Inverse Exponential Distribution (OGE -IED)

Gayus

Doguwa

2022

AJPAS

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The Odd Generalized Exponentiated-Inverse Exponential Distribution, a three parameter distribution, is a hybrid of the Generalized Exponential distribution. Each of the parameters were assigned a gamma prior independently resulting to a posterior distribution that is mathematically intractable impossible to obtain marginal posterior distribution for two of the parameters, and a likelihood function that is not known traditionally to R or other statistical software. Resort was made to STAN in order to obtain Bayesian estimates - leveraging on STAN’s provision for user-defined distribution functions. Two datasets were used; remission times (in months) of bladder cancer patients and COVID-19 Survey data in Andalusia, Spain. In the end, the Maximum Likelihood estimates maximized the likelihood more than the Bayesian estimates - though with a slight margin of not more than 0.77. On the other hand, the Bayesian estimates proved to be more stable yielding very negligible standard errors compared to the Maximum Likelihood estimates.

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