ll giorno lunedì13 febbraio alle ore 11.00 presso 
la aula seminari del Dismeq al IV piano 
dell'edificio U7, il prof. Emilio Porcu del 
Department of Mathematics della University 
Federico Santa Maria di Valparaiso terrà un seminario su
Estimation and Prediction using Wendland 
covariance functions under Infill Asymptotics
Abstract
We study estimation and prediction of Gaussian 
random fields with covariance models belonging to 
the generalized Wendland (GW) class, under fixed 
domain asymptotics. As the Matlern case, this 
class allows a continuous parameterization of 
smoothness of the underlying Gaussian random 
field, being additionally compactly supported. 
The paper is divided into two parts: first, we 
characterize the equivalence of two Gaussian 
measures with GW covariance function, and we 
provide sufficient conditions for the equivalence 
of two Gaussian measures with Matlern and GW 
covariance functions. We elucidate the 
consequences of these facts in terms of 
(misspecified) best linear unbiased predictors. 
In the second part, we establish strong 
consistency and asymptotic distribution of the 
maximum likelihood estimator of the microergodic 
parameter associated to GW covariance model, 
under fixed domain asymptotics. Our findings are 
illustrated through a simulation study: the 
former compares the finite sample behavior of the 
maximum likelihood estimation of the microergodic 
parameter with the given asymptotic distribution. 
The latter compares the finite-sample behavior of 
the prediction and its associated mean square 
error when using two equivalent Gaussian measures 
with Matlern and GW covariance model, using covariance tapering as benchmark.
Tuti gli interessati sono invitati a partecipare.