  For a process governed by a linear Ito stochastic differential equation of
the form dX(t)=[a(t)+b(t)X(t)]dt + \sigma(t)dW(t) we prove an existence of
optimal sampling designs with strictly increasing sampling times. We derive an
asymptotic Fisher information matrix, which we take as a reference in assessing
a quality of finite-point sampling designs. The results are extended to a
broader class of Ito stochastic differential equations satisfying a certain
condition. We give an example based on the Gompertz growth law refuting a
generally accepted opinion that small-sample designs lead to a very high level
of efficiency.
