  We consider the problem of efficient statistical inference for comparing two
regression curves estimated from two samples of dependent measurements. Based
on a representation of the best pair of linear unbiased estimators in
continuous time models as a stochastic integral, an efficient pair of linear
unbiased estimators with corresponding optimal designs for finite sample size
is constructed. This pair minimises the width of the confidence band for the
difference between the estimated curves. We thus extend results readily
available in the literature to the case of correlated observations and provide
an easily implementable and efficient solution. The advantages of using such
pairs of estimators with corresponding optimal designs for the comparison of
regression models are illustrated via numerical examples.
