Publication Date:
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.
Iris type:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
Binary spatial observations, spatial discrimination and
classification, Logistic-Autologistic model, Gibbs sampler.
List of contributors:
Alfo', Marco; Postiglione, Paolo
Book title:
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