Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics

Alessandra Menafoglio and Piercesare Secchi

European Journal of Operational Research, 2017, vol. 258, issue 2, 401-410

Abstract: We review recent advances in Object Oriented Spatial Statistics, a system of ideas, algorithms and methods that allows the analysis of high dimensional and complex data when their spatial dependence is an important issue. At the intersection of different disciplines – including mathematics, statistics, computer science and engineering – Object Oriented Spatial Statistics provides the right perspective to address key problems in varied contexts, from Earth and life sciences to urban planning. We illustrate a few paradigmatic methods applied to problems of prediction, classification and smoothing, giving emphasis to the key ideas Object Oriented Spatial Statistics relies upon.

Keywords: Object oriented data analysis; Kriging for object data; Bagging Voronoi algorithm; Spatial regression models with differential regularization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:258:y:2017:i:2:p:401-410

DOI: 10.1016/j.ejor.2016.09.061

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