Generalized Linear Spatial Models to Predict Slate Exploitability. (6th June 2013)

• Record Type: Journal Article
• Authors: Saavedra, Angeles; Taboada, Javier; Araújo, María; Giráldez, Eduardo
• Other Names: Qiu Zhiping Academic Editor.
• Is Part Of: Journal of applied mathematics. Volume 2013(2013)
• Publication Date: 2013-06-06
• Subjects: Mathematics -- Periodicals; 519.05
• DOI: 10.1155/2013/531062
• Languages: English
• ISSNs: 1110-757X

Abstract

Abstract : The aim of this research was to determine the variables that characterize slate exploitability and to model spatial distribution. A generalized linear spatial model (GLSMs) was fitted in order to explore relationship between exploitability and different explanatory variables that characterize slate quality. Modelling the influence of these variables and analysing the spatial distribution of the model residuals yielded a GLSM that allows slate exploitability to be predicted more effectively than when using generalized linear models (GLM), which do not take spatial dependence into account. Studying the residuals and comparing the prediction capacities of the two models lead us to conclude that the GLSM is more appropriate when the response variable presents spatial distribution.