avviso di seminario

[Marilena Barbieri <marilena.barbieri@uniroma1.it>]

********************************************************

                  AVVISO DI SEMINARIO

********************************************************

Lunedi' 28 maggio 2001 ore 12:00,

il Prof. Bruno Sanso' (Universidad Simon Bolivar,
Caracas - Venezuela),

terra' un seminario dal titolo:

OBJECTIVE BAYESIAN ANALYSIS OF SPATIALLY CORRELATED DATA

presso il Dipartimento di Studi Geoeconomici, Statistici,
Storici per l'Analisi Regionale
Universita' di Roma "La Sapienza", 
Auletta ala Amministrazione,
IV piano edificio Facolta' di Economia, 
via del castro Laurenziano 9.
       
Tutti gli interessati sono invitati a partecipare

					Marilena Barbieri

Abstract: Spatially varying phenomena are often modeled using Gaussian
random fields, specified by their mean function and covariance function.
The spatial correlation structure of these models is commonly specified 
to be of a certain form (e.g., spherical, power exponential, rational
quadratic, or Mat\'ern) with a small
number of unknown parameters. We consider objective Bayesian analysis
of such spatial models, when the mean function of the Gaussian random
field is specified as in a linear model. It is thus necessary to 
determine an objective (or default) prior distribution for the unknown 
mean and covariance parameters of the random field.

We first show that common choices of default prior distributions,
such as the constant prior and the independent Jeffreys prior, typically
result in improper posterior distributions for this model. Next, 
the reference prior for the model is developed, and is shown to
yield a proper posterior distribution. A further attractive property
of the reference prior is that it can be used directly for computation
of Bayes factors or posterior probabilities of hypotheses to compare
different correlation functions, even though the reference prior is 
improper. An illustration is given using a spatial data set of
topographic elevations.