[Forum SIS] Avviso di seminario :: Porcu a DSS (Scienze Statistiche, Sapienza)

[Pierpaolo Brutti]

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 A v v i s o   d i   S e m i n a r i o
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Giovedì 13 Febbraio, ore 15am
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Stanza 34
Dipartimento di Scienze Statistiche
Sapienza Università di Roma

EMILIO PORCU
(Dep. de Estadística, Universidad de Valparaíso, Chile)

terrà un seminario dal titolo

ADAPTIVE TAPERS FOR SPACE-TIME GEOSTATISTICS

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http://seismomathics.wordpress.com

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Maggiori informazioni sui seminari presso il DSS sono
consultabili a quest'indirizzo: http://goo.gl/Y6OQYm

Saluti

Giovanna Jona Lasinio - Pierpaolo Brutti - Fulvio De Santis

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Summary

We propose a class of nonseparable space-time compactly supported
correlation functions, termed here quasi-tapers, to mean that such
correlations can be dynamically compactly supported over space or
time. An important feature of the quasi-taper is given by a spatial
(temporal) compact support being a decreasing function of the temporal
lag (spatial distance), so that the spatial (temporal) compact support
becomes smaller when the temporal (spatial) dependence becomes weaker.
We propose general classes as well as some examples that generalize
the Wendland tapers to the space-time setting. Then a non separable
space-time taper with spatial and temporal compact support is obtained
as tensor product of quasi-tapers. Our space-time taper includes all
the known constructions as special cases and will be shown to have
great exibility. Covariance tapering is then explored as an
alternative to maximum likelihood when estimating the covariance model
of a space-time Gaussian random field in the case of large datasets.
The proposed quasi and space-time tapers allows to perform covariance
tapering when dealing with data that are densely observed in time
(space) but sparse in space (time) or densely observed both in time
and space. The statistical and computational properties of the
space-time covariance tapering is addressed under increasing domain
asymptotics. A simulation study and two real data examples illustrate
the statistical and computational performances of the covariance
tapering with respect to the maximum likelihood method using the
space-time tapers proposed.
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