A Bayesian approach to the estimation of expected cell counts by using log-linear models

Demirhan H., Hamurkaroglu C.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.35, no.2, pp.325-335, 2006 (SCI-Expanded) identifier identifier


In this article, Bayesian estimation of the expected cell counts for log-linear models is considered. The prior specified for log-linear parameters is used to determine a prior for expected cell counts, by means of the family and parameters of prior distributions. This approach is more cost-effective than working directly with cell counts because converting prior information into a prior distribution on the log-linear parameters is easier than that of on the expected cell counts. While proceeding from the prior on log-linear parameters to the prior of the expected cell counts, we faced with a singularity problem of variance matrix of the prior distribution, and added a new precision parameter to solve the problem. A numerical example is also given to illustrate the usage of the new parameter.