Likelihood-free simulation-based optimal design with an application to spatial extremes

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hainy, Markus, Müller, Werner G., Wagner, Helga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2015
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981187/
https://www.ncbi.nlm.nih.gov/pubmed/27563280
http://dx.doi.org/10.1007/s00477-015-1067-8
Descripción
Sumario: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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00477-015-1067-8) contains supplementary material, which is available to authorized users.

Ejemplares similares