Spatially Correlated Model Selection Method - Couverture souple

Velasco-Cruz, Ciro; Leman, Scotland; Smith, Eric

 
9783330344495: Spatially Correlated Model Selection Method
Couverture souple
ISBN 10 : 3330344490 ISBN 13 : 9783330344495
Editeur : LAP LAMBERT Academic Publishing, 2017

Synopsis

A variable selection method for spatial data is presented. It is assumed that the spatial process is non-stationary as a whole but is piece-wise stationary. The pieces where the spatial process is stationary are called regions. The variable selection approach accounts for two sources of correlation: (1) the spatial correlation of the data within the regions, and (2) the correlation of adjacent regions. The variable selection is carried out by including indicator variables that characterize the significance of the regression coefficients. The Ising distribution as prior for the vector of indicator variables, models the dependence of adjacent regions. We present a case study on brook trout data where the response of interest is the pres- ence/absence of the fish at sites in the eastern United States. We find that the method outperforms the case of the probit regression where the spatial field is assumed stationary and isotropic. Additionally, the method outperformed the case where multiple regions are assumed independent of their neighbors.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

A variable selection method for spatial data is presented. It is assumed that the spatial process is non-stationary as a whole but is piece-wise stationary. The pieces where the spatial process is stationary are called regions. The variable selection approach accounts for two sources of correlation: (1) the spatial correlation of the data within the regions, and (2) the correlation of adjacent regions. The variable selection is carried out by including indicator variables that characterize the significance of the regression coefficients. The Ising distribution as prior for the vector of indicator variables, models the dependence of adjacent regions. We present a case study on brook trout data where the response of interest is the pres- ence/absence of the fish at sites in the eastern United States. We find that the method outperforms the case of the probit regression where the spatial field is assumed stationary and isotropic. Additionally, the method outperformed the case where multiple regions are assumed independent of their neighbors.

Biographie de l'auteur

Dr. Ciro Velasco-Cruz is Professor of Statistics in the Stat Program, Colegio de Postgraduados, Mexico. He graduated in 2012 from the Stat Department, Virginia Tech. He is interested in Spatial Statistics, Dynamic Modeling, Bayesian non-parametric modeling.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2017
Langue
anglais
ISBN 10 
3330344490
ISBN 13 
9783330344495
Reliure
Broché
Nombre de pages
124
Coordonnées du fabricant
non disponible
Personne responsable
KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne