  We propose an iterative estimating equations procedure for analysis of
longitudinal data. We show that, under very mild conditions, the probability
that the procedure converges at an exponential rate tends to one as the sample
size increases to infinity. Furthermore, we show that the limiting estimator is
consistent and asymptotically efficient, as expected. The method applies to
semiparametric regression models with unspecified covariances among the
observations. In the special case of linear models, the procedure reduces to
iterative reweighted least squares. Finite sample performance of the procedure
is studied by simulations, and compared with other methods. A numerical example
from a medical study is considered to illustrate the application of the method.
