The implications of loglinear models based on underlying uniform and binomial distribution are assessed with respect to modeling eight distributions. Regarding statistical selection of the loglinear models’ parameterizations, results indicate that better fitting models are obtained when the distribution being modeled is dissimilar to the underlying distribution used. For loglinear models with predetermined numbers of parameters, results suggest that better fitting models can be obtained when the distribution being modeled is similar to the underlying distribution.
"Underlying Distributions in Loglinear Models of Discrete Data,"
Journal of Modern Applied Statistical Methods:
1, Article 2.
Available at: http://digitalcommons.wayne.edu/jmasm/vol11/iss1/2