International Workshop on Spatio - Temporal Statistics Imperial College London 18 – 20 April 2016

s of Talks A Flexible Class of Non-separable Cross-Covariance Functions for Multivariate Space-Time Data Denis Allard L’Institut national de la recherche agronomique Multivariate space-time data are increasingly available in various scientific disciplines. When analyzing these data, one of the key issues is to describe the multivariate space-time dependences. Under the Gaussian framework, one needs to propose relevant models for multivariate space-time covariance functions, i.e. matrixvalued mappings with the additional requirement of non-negative definiteness. A flexible parametric class of cross-covariance functions for multivariate space-time Gaussian random fields is presented. Space-time components belong to the (univariate) Gneiting class of space-time covariance functions, with Matern or Cauchy covariance functions in the spatial margins. The smoothness and scale parameters can be different for each variable. Sufficient conditions for positive definiteness are shown. A simulation study shows that the parameters of this model can be efficiently estimated using weighted pairwise likelihood, which belongs to the class of composite likelihood methods. The model is then illustrated on a French dataset of weather variables. Bootstrapping the roughness index of Brownian semistationary and related processes Mikkel Bennedsen Aarhus University In this talk we are concerned with newly developed bootstrap methods for estimators of the roughness index of a class of continuous, conditionally Gaussian, processes. In particular, we present the local fractional bootstrap of Bennedsen, Hounyo, Lunde and Pakkanen (The local fractional bootstrap, working paper, 2016) and its application to the change-of-frequency estimator of the roughness index of the Brownian semistationary process. The same method is applied in a semiparametric setup to a different estimator of the roughness index in the spirit of Bennedsen (Semiparametric estimation and inference of the fractal index of a time series using the local fractional bootstrap, working paper, 2016). We compare the methods and consider some empirical applications. Geostatistical Modelling Using Non-Gaussian Matérn Fields David Bolin Chalmers University of Technology We present a class of non-Gaussian spatial models useful for analysing geostatistical data. The models are constructed as solutions to stochastic partial differential