Conditional versus Marginal Covariance Representation for
		     Linear and Nonlinear Models

Clustered (or grouped) data, such as repeated measures and
longitudinal data, are increasingly collected in different areas of
application, as varied as clinical trials, epidemilogical studies, and
educational testing. It is often of interest, for these data, to
explore possible relationships between one or more response
variables and available covariates. Because of the within-cluster
correlation typically present with this type of data, special
regression models that allow the joint estimation of mean and
covariance parameters need to be used. Two main approaches have been
proposed to represent the covariance structure of the data with these
models: (i) via the use of random effects, the so-called conditional
model and (ii) through direct representation of the covariance
structure of the responses, known as the marginal approach. In this
talk, we discuss and compare these two approaches in the context of
linear and non-linear regression models with additive Gaussian
errors. Real and simulated data are used to motivate and illustrate
the discussion.