Ford, Ian; Titterington, D.M.; Kitsos, Christos P. (1989, ASQC and the American Statistical Association) University of Glasgow, Scotland; Technological Educational Institute of Athens, Greece
This article summarizes recent work in optimal experimental design in nonlinear problems, in which the major difficulty in obtaining good or optimal designs is their dependence on the true value of the parameters. This difficulty arises in problems with nonlinear models or with linear models in which interest lies in a nonlinear function of the parameters. Most approaches use a static design based on "prior" information about the parameters or a sequential procedure that takes advantage of the inflow of new information about them. The various versions of these methods are discussed, as are some of the consequent problems of inference. Some selected procedures are compared using simulation studies.
Regression,Optimal design,Statistics,Sequential methods