:: Volume 1, Issue 1 (9-2004) ::
JSRI 2004, 1(1): 31-50 Back to browse issues page
Regression Analysis under Inverse Gaussian Model: Repeated Observation Case
Reza Meshkani M. *
, mrmeshkani@gmail.com
Abstract:   (2562 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.

Keywords: Bayesian inference, empirical Bayes, conjugate prior, posterior, inverse Gaussian distribution, regression, likelihood.
Full-Text [PDF 1349 kb]   (589 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/02/9 | Accepted: 2016/02/9 | Published: 2016/02/9



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Volume 1, Issue 1 (9-2004) Back to browse issues page