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.