University of Southampton, Southampton Statistical Sciences Research Institute
Doi
Abstract
In this paper, we investigate optimal designs for multivariate additive spline regressionmodels. We assume that the knot locations are unknown, so must be estimated from thedata. In this situation, the Fisher information for the full parameter vector depends on theunknown knot locations, resulting in a non-linear design problem. We show that locally,Bayesian and maximin D-optimal designs can be found as the products of the optimaldesigns in one dimension. A similar result is proven for Q-optimality in the class of allproduct design
