On the goodness-of-fit of generalized linear geostatistical models

Emanuele Giorgi

doi:10.1016/j.spasta.2018.01.002

On the goodness-of-fit of generalized linear geostatistical models

Emanuele Giorgi

Applied Health Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

We propose a generalization of Zhang's coefficient of determination to generalized linear geostatistical models and illustrate its application to river-blindness mapping. The generalized coefficient of determination has a more intuitive interpretation than other measures of predictive performance and allows to assess the individual contribution of each explanatory variable and the random effects to spatial prediction. The developed methodology is also more widely applicable to any generalized linear mixed model.

Original language	English
Pages (from-to)	79-83
Number of pages	5
Journal	Spatial Statistics
Volume	28
DOIs	https://doi.org/10.1016/j.spasta.2018.01.002
Publication status	Published - Dec 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier B.V.

Keywords

Coefficient of determination
Generalized linear geostatistical models
Goodness-of-fit

ASJC Scopus subject areas

Statistics and Probability
Computers in Earth Sciences
Management, Monitoring, Policy and Law

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10.1016/j.spasta.2018.01.002

Cite this

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abstract = "We propose a generalization of Zhang's coefficient of determination to generalized linear geostatistical models and illustrate its application to river-blindness mapping. The generalized coefficient of determination has a more intuitive interpretation than other measures of predictive performance and allows to assess the individual contribution of each explanatory variable and the random effects to spatial prediction. The developed methodology is also more widely applicable to any generalized linear mixed model.",

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AB - We propose a generalization of Zhang's coefficient of determination to generalized linear geostatistical models and illustrate its application to river-blindness mapping. The generalized coefficient of determination has a more intuitive interpretation than other measures of predictive performance and allows to assess the individual contribution of each explanatory variable and the random effects to spatial prediction. The developed methodology is also more widely applicable to any generalized linear mixed model.

KW - Coefficient of determination

KW - Generalized linear geostatistical models

KW - Goodness-of-fit

UR - https://www.scopus.com/pages/publications/85041613037

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DO - 10.1016/j.spasta.2018.01.002

M3 - Article

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SN - 2211-6753

VL - 28

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JO - Spatial Statistics

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ER -

On the goodness-of-fit of generalized linear geostatistical models

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this