Regression Analysis under Inverse Gaussian Model: Repeated Observation Case
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Reza Meshkani |
, mrmeshkani@gmail.com |
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Abstract: (3186 Views) |
Traditional regression analyses assume normality of observations and independence of mean and variance. However, there are many examples in science and Technology where the observations come from a skewed distribution and moreover there is a functional dependence between variance and mean.
In this article, we propose a method for regression analysis under Inverse Gaussian model when there are repeated observations for a fixed value of explanatory variable. The problem is treated by likelihood, Bayes, and empirical Bayes procedures, using conjugate priors. Inferences are provided for regression analysis.
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Keywords: Bayesian inference, empirical Bayes, conjugate prior, posterior, inverse Gaussian distribution, regression, likelihood. |
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Full-Text [PDF 1349 kb]
(924 Downloads)
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Type of Study: Research |
Subject:
General Received: 2016/02/9 | Accepted: 2016/02/9 | Published: 2016/02/9
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