Citation

Menafoglio A, Secchi P. Eur. J. Oper. Res. 2017; 258(2): 401-410.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.ejor.2016.09.061

PMID

unavailable

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.

Language: en

Keywords

Bagging Voronoi algorithm; Kriging for object data; Object oriented data analysis; Spatial regression models with differential regularization