We study a new approach to determine optimal designs, exact or approximate, both for the uncorrelated case and when the responses may be correlated. A simple version of this method is based on transforming design points on a finite interval to proportions of the interval. Methods for determining optimal design weights can therefore be used to determine optimal values of these proportions. We explore the potential of this method in a range of examples encompassing linear and non-linear models, some assuming a correlation structure and some with more than one design variable
A challenge in engineering design is to choose suitable objectives and constraints from many quantit...
Some new properties and computational tools for finding KL-optimum designs are provided in this pape...
Finding optimal designs for nonlinear models is challenging in general. Although some recent results...
In this paper we discuss a class of multiplicative algorithms for computing D-optimal designs for re...
A class of multiplicative algorithms for computing DD-optimal designs for regression models on a fin...
International audienceBackground and objectives: To optimize designs for longitudinal studies analyz...
Chapter 1 provides an introduction to the area of optimum experimental design for the linear regress...
International audienceBackground and objectives: To optimize designs for longitudinal studies analyz...
The problem of finding optimal exact designs is more challenging than that of approximate optimal de...
We construct approximate optimal designs for minimising absolute covariances between least-squares e...
We study a class of multiplicative algorithms introduced by Silvey et al. (1978) for computing D-op...
A method that makes use of combinatorics for selecting N objects out of distinguishable objects is d...
For many statistical experiments, there exists a multitude of optimal designs. If we consider models...
Chapter 1 provides an introduction to the area of optimum experimental design for the linear regress...
The basic problem considered in this paper may be stated as follows: find an N-point exact design me...
A challenge in engineering design is to choose suitable objectives and constraints from many quantit...
Some new properties and computational tools for finding KL-optimum designs are provided in this pape...
Finding optimal designs for nonlinear models is challenging in general. Although some recent results...
In this paper we discuss a class of multiplicative algorithms for computing D-optimal designs for re...
A class of multiplicative algorithms for computing DD-optimal designs for regression models on a fin...
International audienceBackground and objectives: To optimize designs for longitudinal studies analyz...
Chapter 1 provides an introduction to the area of optimum experimental design for the linear regress...
International audienceBackground and objectives: To optimize designs for longitudinal studies analyz...
The problem of finding optimal exact designs is more challenging than that of approximate optimal de...
We construct approximate optimal designs for minimising absolute covariances between least-squares e...
We study a class of multiplicative algorithms introduced by Silvey et al. (1978) for computing D-op...
A method that makes use of combinatorics for selecting N objects out of distinguishable objects is d...
For many statistical experiments, there exists a multitude of optimal designs. If we consider models...
Chapter 1 provides an introduction to the area of optimum experimental design for the linear regress...
The basic problem considered in this paper may be stated as follows: find an N-point exact design me...
A challenge in engineering design is to choose suitable objectives and constraints from many quantit...
Some new properties and computational tools for finding KL-optimum designs are provided in this pape...
Finding optimal designs for nonlinear models is challenging in general. Although some recent results...