Flexible Distributed Lag Models for Count Data Using mgcv.
In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model che...
| Publicado en: | American Statistician Vol. 79; no. 3; pp. 371 - 383 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Aug2025
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| Materias: | |
| 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=187004685&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187004685 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: Aug2025 vid: 79 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 187004685 10.1080/00031305.2025.2505514 ppf: 371 ppct: 12 formats: tig: atl: Flexible Distributed Lag Models for Count Data Using mgcv. aug: au: Economou, Theo Parliari, Daphne Tobias, Aurelio Dawkins, Laura Steptoe, Hamish Sarran, Christophe Stoner, Oliver Lowe, Rachel Lelieveld, Jos affil: Department of Mathematics and Statistics, University of Exeter, Exeter, UK Climate and Atmosphere Research, The Cyprus Institute, Nicosia, Cyprus Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece Institute for Environmental Assessment and Water Research, Spanish National Research Council, Barcelona, Spain Met Office, Exeter, UK School of Mathematics and Statistics, University of Glasgow, Glasgow, UK Barcelona Supercomputing Center (BSC), Barcelona, Spain Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain London School of Hygiene & Tropical Medicine, London, UK Max Planck Institute for Chemistry, Mainz, Germany su: Epidemiology Statistical models Spatial variation Mixture distributions (Probability theory) Bayesian analysis sug: subj: Epidemiology Statistical models Spatial variation Mixture distributions (Probability theory) Bayesian analysis keyword: Bayesian inference DLNM Environmental epidemiology Heat-stress Penalized splines Bayesian inference DLNM Environmental epidemiology Heat-stress Penalized splines ab: In this tutorial we present the use of R package mgcv to implement Distributed Lag Non-Linear Models (DLNMs) in a flexible way. Interpretation of smoothing splines as random quantities enables approximate Bayesian inference, which in turn allows uncertainty quantification and comprehensive model checking. We illustrate various modeling situations using open-access epidemiological data in conjunction with simulation experiments. We demonstrate the inclusion of temporal structures and the use of mixture distributions to allow for extreme outliers. Moreover, we demonstrate interactions of the temporal lagged structures with other covariates with different lagged periods for different covariates. Spatial structures are also demonstrated, including smooth spatial variability and Markov random fields, in addition to hierarchical formulations to allow for non-structured dependency. Posterior predictive simulation is used to ensure models verify well against the data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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