Data di Pubblicazione:
1999
Abstract:
Spatially distributed observations occur naturally in a number of
empirical situations; their analysis represents a significant source of theoretical
challenge due to the multidirectional dependence among nearest observations.
The presence of a dependence often causes the standard statistical methods,
instead based on independence assumptions, to fail badly. This paper concerns
the problem of discrimination and classification of spatial binary data. It
presents a suitable discrimination function based on Markovian automodels and
suggests a solution to the allocation problem through a Gibbs sampler-based
procedure.
empirical situations; their analysis represents a significant source of theoretical
challenge due to the multidirectional dependence among nearest observations.
The presence of a dependence often causes the standard statistical methods,
instead based on independence assumptions, to fail badly. This paper concerns
the problem of discrimination and classification of spatial binary data. It
presents a suitable discrimination function based on Markovian automodels and
suggests a solution to the allocation problem through a Gibbs sampler-based
procedure.
Tipologia CRIS:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
Binary spatial observations, spatial discrimination and
classification, Logistic-Autologistic model, Gibbs sampler.
Elenco autori:
Alfo', Marco; Postiglione, Paolo
Link alla scheda completa:
Titolo del libro:
Classification and data analysis : theory and application : proceedings of the Biannual meeting of the Classification group of Societa italiana di statistica (SIS), Pescara, July 3-4, 1997 / Maurizio Vichi, Otto Opitz editors