Generalized Linear Spatial Models to Predict Slate Exploitability

被引：0

作者：

Saavedra, Angeles ^{[1
,2
]}

Taboada, Javier ^{[3
]}

Araujo, Mara ^{[3
]}

Giraldez, Eduardo ^{[3
]}

机构：

[1] Univ Vigo, Dept Stat, Vigo 36310, Spain

[2] Univ Vigo, ETSI MINAS, Vigo 36310, Spain

[3] Univ Vigo, Dept Nat Resources, Vigo 36310, Spain

来源：

JOURNAL OF APPLIED MATHEMATICS | 2013年

关键词：

MIXED MODELS; BAYESIAN PREDICTION; GEOSTATISTICS;

D O I：

10.1155/2013/531062

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页数：7

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