title: Likelihood-free simulation-based optimal design with an application to spatial extremes.
abstract: In this paper we employ a novel method to find the optimal design for problems where the likelihood is not available analytically, but simulation from the likelihood is feasible. To approximate the expected utility we make use of approximate Bayesian computation methods. We detail the approach for a model on spatial extremes, where the goal is to find the optimal design for efficiently estimating the parameters determining the dependence structure. The method is applied to determine the optimal design of weather stations for modeling maximum annual summer temperatures.
authors: Department of Applied Statistics, Johannes Kepler University, Altenberger Strasse 69, 4040 Linz, Austria.
authors: Markus
authors: M
authors: Hainy
authors: Department of Applied Statistics, Johannes Kepler University, Altenberger Strasse 69, 4040 Linz, Austria.
authors: Werner G
authors: WG
authors: Müller
authors: Department of Applied Statistics, Johannes Kepler University, Altenberger Strasse 69, 4040 Linz, Austria.
authors: Helga
authors: H
authors: Wagner
keywords: Approximate Bayesian computation; Importance sampling; Max-stable processes; Simulation-based optimal design; Spatial extremes