  Experimental designs based on the classical D-optimal criterion minimize the
volume of the linear-approximation inference regions for the parameters using
local sensitivity coefficients. For nonlinear models, these designs can be
unreliable because the linearized inference regions do not always provide a
true indication of the exact parameter inference regions. In this article, we
apply the profile-based sensitivity coefficients developed by Sulieman et.al.
[12] in designing D-optimal experiments for parameter estimation in some
selected nonlinear models. Profile-based sensitivity coefficients are defined
by the total derivative of the model function with respect to the parameters.
They have been shown to account for both parameter co-dependencies and model
nonlinearity up to second order-derivative. This work represents a first
attempt to construct experiments using profile-based sensitivity coefficients.
Two common nonlinear models are used to illustrate the computational aspects of
the profile-based designs and simulation studies are conducted to demonstrate
the efficiency of the constructed experiments.
