Spatio-temporal statistical methods are developing into an important research topic that goes beyond the study of processes that generate independent, identically distributed observations. Hierarchical models are a suitable proposal for both continuous and discrete spatio-temporal domains. They are flexible and permit separation of the various sources of uncertainty by means of a sequence of conditional models. In this work, we expanded on spatio-temporal data modeling by considering data categorization with respect to certain differentiating features. We studied the impact of the presence of subgroups on model building, with emphasis on Bayesian modeling. We discussed how differences in spatial locations can be reflected in a hierarchical model and assessed the performances of different models via a simulation study.

Considering groups in the statistical modeling of spatio-temporal data

COCCHI, DANIELA;BRUNO, FRANCESCA
2010

Abstract

Spatio-temporal statistical methods are developing into an important research topic that goes beyond the study of processes that generate independent, identically distributed observations. Hierarchical models are a suitable proposal for both continuous and discrete spatio-temporal domains. They are flexible and permit separation of the various sources of uncertainty by means of a sequence of conditional models. In this work, we expanded on spatio-temporal data modeling by considering data categorization with respect to certain differentiating features. We studied the impact of the presence of subgroups on model building, with emphasis on Bayesian modeling. We discussed how differences in spatial locations can be reflected in a hierarchical model and assessed the performances of different models via a simulation study.
2010
D. Cocchi; F. Bruno
File in questo prodotto:
File Dimensione Formato  
Statistica4_10pp.511-527.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 267.58 kB
Formato Adobe PDF
267.58 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/106843
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact