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Article: Geographically weighted temporally correlated logistic regression model

TitleGeographically weighted temporally correlated logistic regression model
Authors
Issue Date2018
PublisherNature Publishing Group: Open Access Journals - Option C. The Journal's web site is located at http://www.nature.com/srep/index.html
Citation
Scientific Reports, 2018, v. 8 n. 1, article no. 1417(2018) How to Cite?
AbstractDetecting the temporally and spatially varying correlations is important to understand the biological and disease systems. Here we proposed a geographically weighted temporally correlated logistic regression (GWTCLR) model to identify such dynamic correlation of predictors on binomial outcome data, by incorporating spatial and temporal information for joint inference. The local likelihood method is adopted to estimate the spatial relationship, while the smoothing method is employed to estimate the temporal variation. We present the construction and implementation of GWTCLR and the study of the asymptotic properties of the proposed estimator. Simulation studies were conducted to evaluate the robustness of the proposed model. GWTCLR was applied on real epidemiologic data to study the climatic determinants of human seasonal influenza epidemics. Our method obtained results largely consistent with previous studies but also revealed certain spatial and temporal varying patterns that were unobservable by previous models and methods.
Persistent Identifierhttp://hdl.handle.net/10722/262251
ISSN
2017 Impact Factor: 4.122
2015 SCImago Journal Rankings: 2.073
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Y-
dc.contributor.authorLam, KF-
dc.contributor.authorWu, JTK-
dc.contributor.authorLam, TY-
dc.date.accessioned2018-09-28T04:56:03Z-
dc.date.available2018-09-28T04:56:03Z-
dc.date.issued2018-
dc.identifier.citationScientific Reports, 2018, v. 8 n. 1, article no. 1417(2018)-
dc.identifier.issn2045-2322-
dc.identifier.urihttp://hdl.handle.net/10722/262251-
dc.description.abstractDetecting the temporally and spatially varying correlations is important to understand the biological and disease systems. Here we proposed a geographically weighted temporally correlated logistic regression (GWTCLR) model to identify such dynamic correlation of predictors on binomial outcome data, by incorporating spatial and temporal information for joint inference. The local likelihood method is adopted to estimate the spatial relationship, while the smoothing method is employed to estimate the temporal variation. We present the construction and implementation of GWTCLR and the study of the asymptotic properties of the proposed estimator. Simulation studies were conducted to evaluate the robustness of the proposed model. GWTCLR was applied on real epidemiologic data to study the climatic determinants of human seasonal influenza epidemics. Our method obtained results largely consistent with previous studies but also revealed certain spatial and temporal varying patterns that were unobservable by previous models and methods.-
dc.languageeng-
dc.publisherNature Publishing Group: Open Access Journals - Option C. The Journal's web site is located at http://www.nature.com/srep/index.html-
dc.relation.ispartofScientific Reports-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleGeographically weighted temporally correlated logistic regression model-
dc.typeArticle-
dc.identifier.emailLam, KF: hrntlkf@hkucc.hku.hk-
dc.identifier.emailWu, JTK: joewu@hku.hk-
dc.identifier.emailLam, TY: ttylam@hku.hk-
dc.identifier.authorityLam, KF=rp00718-
dc.identifier.authorityWu, JTK=rp00517-
dc.identifier.authorityLam, TY=rp01733-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/s41598-018-19772-6-
dc.identifier.pmid29362396-
dc.identifier.scopuseid_2-s2.0-85041022139-
dc.identifier.hkuros293171-
dc.identifier.volume8-
dc.identifier.issue1-
dc.identifier.spagearticle no. 1417(2018)-
dc.identifier.epagearticle no. 1417(2018)-
dc.identifier.isiWOS:000423044400033-
dc.publisher.placeUnited Kingdom-

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