Home > Research > Publications & Outputs > On the goodness-of-fit of generalized linear ge...

Research

Associated organisational unit

Electronic data

  • 801.04111

    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.01.002

    Accepted author manuscript, 290 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

Keywords

View graph of relations

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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

On the goodness-of-fit of generalized linear geostatistical models. / Giorgi, Emanuele.
In: Spatial Statistics, Vol. 28, 12.2018, p. 79-83.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Giorgi, E 2018, 'On the goodness-of-fit of generalized linear geostatistical models', Spatial Statistics, vol. 28, pp. 79-83. https://doi.org/10.1016/j.spasta.2018.01.002

APA

Giorgi, E. (2018). On the goodness-of-fit of generalized linear geostatistical models. Spatial Statistics, 28, 79-83. https://doi.org/10.1016/j.spasta.2018.01.002

Vancouver

Giorgi E. On the goodness-of-fit of generalized linear geostatistical models. Spatial Statistics. 2018 Dec;28:79-83. Epub 2018 Feb 12. doi: 10.1016/j.spasta.2018.01.002

Author

Giorgi, Emanuele. / On the goodness-of-fit of generalized linear geostatistical models. In: Spatial Statistics. 2018 ; Vol. 28. pp. 79-83.

Bibtex

@article{a5483b3dd880462e99b1c0001c644cf3,
title = "On the goodness-of-fit of generalized linear geostatistical models",
abstract = "We propose a generalization of Zhang{\textquoteright}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.",
keywords = "Coefficient of determination, Generalized linear geostatistical models, Goodness-of-fit",
author = "Emanuele Giorgi",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.01.002",
year = "2018",
month = dec,
doi = "10.1016/j.spasta.2018.01.002",
language = "English",
volume = "28",
pages = "79--83",
journal = "Spatial Statistics",
issn = "2211-6753",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - On the goodness-of-fit of generalized linear geostatistical models

AU - Giorgi, Emanuele

N1 - This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.01.002

PY - 2018/12

Y1 - 2018/12

N2 - 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.

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

U2 - 10.1016/j.spasta.2018.01.002

DO - 10.1016/j.spasta.2018.01.002

M3 - Journal article

VL - 28

SP - 79

EP - 83

JO - Spatial Statistics

JF - Spatial Statistics

SN - 2211-6753

ER -