In this paper we discuss a class of multiplicative algorithms for computing D-optimal designs for regression models on a finite design space. We prove amonotonicity result for a sequence of determinants obtained by the iterations,and as a consequence the procedure yields a sequence of designs...

The problem of determining optimal designs for least squares estimation is considered in the common linear regression model with correlated observations. The approach is based on the determination of ‘nearly’ universally optimal designs, even in the case where the universally optimal design...

A class of multiplicative algorithms for computing D-optimal designs for regression models on a finite design space is discussed and a monotonicity result for a sequence of determinants obtained by the iterations is proved. As a consequence the convergence of the sequence of designs to the...

In this paper we discuss a class of multiplicative algorithms for computing D-optimal designs for regression models on a finite design space. We prove amonotonicity result for a sequence of determinants obtained by the iterations,and as a consequence the procedure yields a sequence of designs...

In this paper we discuss a class of multiplicative algorithms for computing D-optimal designs for regression models on a finite design space. We prove amonotonicity result for a sequence of determinants obtained by the iterations,and as a consequence the procedure yields a sequence of designs...

We consider the problem of predicting values of a random process or field satisfying a linear model y(x)=θ⊤f(x)+ε(x), where errors ε(x)are correlated. This is a common problem in kriging, where the case of discrete observations is standard. By focussing on the case of continuous...

Toxicologists have been increasingly using a class of models to describe a continuous response in the last few years. This class consists of nested nonlinear models and is used for estimating various parameters in the models or some meaningful function of the model parameters. Our work here is...