This paper studies the model-robust design problem for general models with an unknown bias or contamination and the correlated errors. The true response function is assumed to be from a reproducing kernel Hilbert space and the errors are fitted by the qth order moving average process MA（q）, especially the MA（1） errors and the MA（2） errors. In both situations, design criteria are derived in terms of the average expected quadratic loss for the least squares estimation by using a minimax method. A case is studied and the orthogonality of the criteria is proved for this special response. The robustness of the design criteria is discussed through several numerical examples.
This paper deals with the problem of P-optimal robust designs for multiresponse approximately linear regression models. Each response is assumed to be only approximately linear in the regressors, and the bias function varies over a given L2--neighbourhood. A kind of bivariate models with two responses is taken as an example to illustrate how to get the expression of the design measure.
