[Forum SIS] Rinvio seminario prof.Roberto Benedetti "A Design for Spatial Finite Populations Based on the Within Sample Distance”
Caterina Giusti
caterina.giusti a ec.unipi.it
Sab 24 Nov 2012 14:31:26 CET
Si avvisa che il seminario del prof. Roberto Benedetti “A Design for
Spatial Finite Populations Based on the Within Sample Distance”,
previsto per il giorno mercoledì 28 novembre 2012 alle ore 10:30 nella
Sala Bruguier Pacini del Dipartimento di Economia e Management
dell'Università di Pisa, è stato rinviato al giorno 12 dicembre alle ore
10:30.
Tutti gli interessati sono invitati a partecipare.
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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
Via C. Ridolfi 10 - 56124 Pisa
tel: +39 (0)50 2216 225
fax: +39 (0)50 2216 375
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