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Generalized Linear Spatial Models to Predict Slate Exploitability
Joint Authors
Taboada, Javier
Giráldez, Eduardo
Araújo, María
Saavedra, Angeles
Source
Journal of Applied Mathematics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-06
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Saavedra, Angeles& Taboada, Javier& Araújo, María& Giráldez, Eduardo. 2013. Generalized Linear Spatial Models to Predict Slate Exploitability. Journal of Applied Mathematics،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-479142
Modern Language Association (MLA)
Saavedra, Angeles…[et al.]. Generalized Linear Spatial Models to Predict Slate Exploitability. Journal of Applied Mathematics No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-479142
American Medical Association (AMA)
Saavedra, Angeles& Taboada, Javier& Araújo, María& Giráldez, Eduardo. Generalized Linear Spatial Models to Predict Slate Exploitability. Journal of Applied Mathematics. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-479142
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-479142