[Forum SIS] Avviso seminario (“A Design for Spatial Finite Populations Based on the Within Sample Distance” )

[Caterina Giusti]

Scusate mi hanno segnalato la mia e-mail precedente non conteneva le 
informazioni sul seminario.
Le riporto qui di seguito.

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Pisa, 20/11/2012

         Il giorno mercoledì 28 novembre 2012 alle ore 10:30, nella Sala 
Bruguier Pacini del Dipartimento di Economia e Management (terzo piano), 
il prof. Roberto Benedetti, docente presso l’Università Gabriele 
D’Annunzio di Pescara, terrà un seminario dal titolo:

“A Design for Spatial Finite Populations Based on the Within Sample 
Distance”

Tutti gli interessati sono invitati a partecipare.


                                                     Il responsabile 
dell’organizzazione
                                                                       
dei seminari
                                                              Prof. 
Paolo Scapparone


Allego l’abstract del lavoro che sarà presentato nel seminario.

Maria Angela Magi
Dipartimento di Economia e Management
Via Ridolfi, 10
56124 Pisa

Tel. 050 2216466
Fax 050 2216384

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A Design for Spatial Finite Populations Based on the Within Sample Distance

R. Benedetti
 “G. d’Annunzio” University,
Department of Economics (DEc),
Viale Pindaro 42, Pescara, IT-65127, Italy
benedett a unich.it

R. L. Chambers
Centre for Statistical and Survey Methodology,
School of Mathematics and Applied Statistics,
University of Wollongong, Wollongong, NSW 2522, Australia
ray a uow.edu.au

F. Piersimoni
Istat, Italian National Institute of Statistics, Agricultural 
Statistical Service,
Viale Oceano Pacifico 171, Rome, IT-00144, Italy
piersimo a istat.it

Abstract: A method for selecting samples from a spatial finite 
population that are well spread over the population in every dimension, 
without the use of any spatial stratification is presented. The within 
sample distance matrix is summarized in a descriptive index which is 
used to define the probability p(s) of each sample to be selected. A set 
of units with higher within distance will be selected with higher 
probability than a set of nearby units. Through the standardization of 
the distance matrix the method can be used to produce equal and unequal 
probability samples either exact when a linear index is used to 
summarize the matrix or approximate when products and powers of the mean 
are used. The high flexibility of the selection algorithm can make 
possible numerous extensions to deal with some practical topics that are 
usually met in spatial surveys such as the sample coordination and the 
spread of units belonging to different domains. Some examples on real 
and simulated data show that the method gives estimates that are better 
than those obtained by using a classical solution as the Generalized 
Random Tessellation Stratified (GRTS) design and that often are even 
slightly better than those obtained by using recently proposed selection 
procedures as the Spatially Correlated Poisson Sampling (SCPS) and the 
Pivotal method.
With respect to these distance based methods the proposed algorithm, 
even if in its nature it is computationally intensive, seems to be a 
faster solution particularly when dealing with large population frames.

Keywords: spatially balanced samples, empirical inclusion probabilities, 
MCMC, Correlated Poisson Sampling, Pivotal Method, Generalized Random 
Tessellation Stratified design.


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Caterina Giusti
Dipartimento di Economia e Management
Università di Pisa
tel: +39 (0)50 2216 225
fax: +39 (0)50 2216 375
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