Computational Statistics & Data Analysis vol:57 pages:233-245
In sets of count data, the sample variance is often considerably larger or smaller than the
sample mean, known as a problem of over- or underdispersion. The focus is on hierarchical
Bayesian modeling of such longitudinal count data. Two different models are considered.
The first one assumes a Poisson distribution for the count data and includes a subjectspecific
intercept, which is assumed to follow a normal distribution, to account for subject
heterogeneity. However, such a model does not fully address the potential problem of
extra-Poisson dispersion. The second model, therefore, includes also random subject and
time dependent parameters, assumed to be gamma distributed for reasons of conjugacy.
To compare the performance of the two models, a simulation study is conducted in which
the mean squared error, relative bias, and variance of the posterior means are compared.