Robustness of Bayesian results for Inverse Gaussian distribution under ML-II epsilon-contaminated and Edgeworth Series class of prior distributions

Pankaj Sinha and Prabha Jayaraman

MPRA Paper from University Library of Munich, Germany

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 two-sided predictive limits for a future observation from IG population. Two prior distributions, a class ML-II ε-contaminated 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 ML-II ε-contaminated and ESD does not affect the Bayesian results.

Keywords: Bayesian results; Inverse Gaussian distribution; ML-II ε-contaminated prior; Edgeworth Series Distributions (search for similar items in EconPapers)
JEL-codes: A10 C01 C02 C11 C44 C46 (search for similar items in EconPapers)
Date: 2009-05-17
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/15396/1/MPRA_paper_15396.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:15396

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().