RT - Journal Article
T1 - Regression Analysis under Inverse Gaussian Model: Repeated Observation Case
JF - srtc-jsri
YR - 2004
JO - srtc-jsri
VO - 1
IS - 1
UR - http://jsri.srtc.ac.ir/article-1-138-en.html
SP - 31
EP - 50
K1 - Bayesian inference
K1 - empirical Bayes
K1 - conjugate prior
K1 - posterior
K1 - inverse Gaussian distribution
K1 - regression
K1 - likelihood.
AB - 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.
LA eng
UL http://jsri.srtc.ac.ir/article-1-138-en.html
M3 10.18869/acadpub.jsri.1.1.31
ER -