"Gibbs sampling method for linear model selection"
Honours Project Seminar
by Sen Tan
Abstract: In modeling a linear model, when the number of potential explanatory variables increases, the number of candidate models increases exponentially. How to overcome this computation difficulty is essential for linear model selection.
In this paper I use a MCMC method---Gibbs sampling method to implement two commonly used model selection criteria AIC and BIC for linear regression model selection where very large number of candidate models are involved, and compare it with other commonly used algorithm such as stepwise and leap and bounds method.
For More Information: Associate Professor Felisa J. VÃ¡zquez-Abad