Sinha, Pankaj and Jayaraman, Prabha (2009): Robustness of Bayesian results for Inverse Gaussian distribution under MLII epsiloncontaminated and Edgeworth Series class of prior distributions.

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Abstract
This paper aims to study the sensitivity of Bayes estimate of location parameter of an Inverse Gaussian (IG) distribution to misspecification in the prior distribution. It also studies the effect of misspecification of the prior distribution on twosided predictive limits for a future observation from IG population. Two prior distributions, a class MLII εcontaminated and Edgeworth Series (ESD), are employed for the location parameter of an IG distribution, to investigate the effect of misspecification in the priors. The numerical illustrations suggest that moderate amount of misspecification in prior distributions belonging to the class of MLII εcontaminated and ESD does not affect the Bayesian results.
Item Type:  MPRA Paper 

Original Title:  Robustness of Bayesian results for Inverse Gaussian distribution under MLII epsiloncontaminated and Edgeworth Series class of prior distributions 
Language:  English 
Keywords:  Bayesian results,Inverse Gaussian distribution,MLII εcontaminated prior,Edgeworth Series Distributions 
Subjects:  C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C44  Operations Research ; Statistical Decision Theory C  Mathematical and Quantitative Methods > C0  General > C02  Mathematical Methods C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C46  Specific Distributions ; Specific Statistics A  General Economics and Teaching > A1  General Economics > A10  General C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  15396 
Depositing User:  Pankaj Sinha 
Date Deposited:  26. May 2009 00:04 
Last Modified:  28. Apr 2015 11:45 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/15396 