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Activity Number: 305 - Bayesian Modeling and Variable Selection Methods
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #304963 Presentation
Title: A New Generalized Inverse Gaussian Distribution with Bayesian Estimators
Author(s): Kenneth R Goward* and Chin-I Cheng and Kahadawala Cooray
Companies: Central Michigan University and Central Michigan University and Central Michigan University
Keywords: Bayesian analysis; Inverse Gaussian distribution; Maximum likelihood estimation; Metropolis-Hastings algorithm

A four-parameter family of transformed inverse Gaussian (TIG) distribution is described. A three-parameter family derived from the four-parameter TIG family is considered, with a specific new distribution referred to as the Generalized inverse Gaussian (GIG) distribution being considered. Two different versions of this distribution are provided and computational and theoretical advantages of one over the other are discussed. Maximum likelihood techniques are discussed alongside Bayesian approaches with Jeffreys-type priors for parameter estimation. A simulation study was conducted and results from the Bayesian approach and approximations to the maximum likelihood estimators were analyzed using the Kolmogorov-Smirnov test. The applicability of this distribution is considered on a real world data set.

Authors who are presenting talks have a * after their name.

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