Fitting Log-Gaussian Cox Processes Using Generalized Additive Model Software.
While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show that, for suitably formatted data, we can actually fit these models using generalized additive model soft...
| Publicado en: | American Statistician Vol. 78; no. 4; pp. 418 - 426 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Nov2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=180359693&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 180359693 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Nov2024 vid: 78 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 180359693 10.1080/00031305.2024.2316725 ppf: 418 ppct: 8 formats: tig: atl: Fitting Log-Gaussian Cox Processes Using Generalized Additive Model Software. aug: au: Dovers, Elliot Stoklosa, Jakub Warton, David I. affil: School of Mathematics and Statistics and Evolution & Ecology Research Centre, UNSW Sydney, Sydney, NSW, Australia su: Random fields Computer software quality control Point processes Regression analysis Research personnel Gaussian processes sug: subj: Random fields Computer software quality control Point processes Regression analysis Research personnel Gaussian processes keyword: Basis functions Generalized additive model Spatial statistics Basis functions Generalized additive model Spatial statistics ab: While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show that, for suitably formatted data, we can actually fit these models using generalized additive model software, via a simple line of code, demonstrated on R by the popular mgcv package. We are able to do this because a common and computationally efficient way to fit a log-Gaussian Cox process model is to use a basis function expansion to approximate the Gaussian random field, as is provided by a generic bivariate smoother over geographic space. We further show that if basis functions are parameterized appropriately then we can estimate parameters in the spatial covariance function for the latent random field using a generalized additive model. We use simulation to show that this approach leads to model fits of comparable quality to state-of-the-art software, often more quickly. But we see the main advance from this work as lowering the technology barrier to spatial statistics for applied researchers, many of whom are already familiar with generalized additive model software. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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