File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1038/s41598-018-19772-6
- Scopus: eid_2-s2.0-85041022139
- PMID: 29362396
- WOS: WOS:000423044400033
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Geographically weighted temporally correlated logistic regression model
Title | Geographically weighted temporally correlated logistic regression model |
---|---|
Authors | |
Issue Date | 2018 |
Publisher | Nature 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? |
Abstract | Detecting 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 Identifier | http://hdl.handle.net/10722/262251 |
ISSN | 2021 Impact Factor: 4.996 2020 SCImago Journal Rankings: 1.240 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Liu, Y | - |
dc.contributor.author | Lam, KF | - |
dc.contributor.author | Wu, JTK | - |
dc.contributor.author | Lam, TY | - |
dc.date.accessioned | 2018-09-28T04:56:03Z | - |
dc.date.available | 2018-09-28T04:56:03Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Scientific Reports, 2018, v. 8 n. 1, article no. 1417(2018) | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262251 | - |
dc.description.abstract | Detecting 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.language | eng | - |
dc.publisher | Nature Publishing Group: Open Access Journals - Option C. The Journal's web site is located at http://www.nature.com/srep/index.html | - |
dc.relation.ispartof | Scientific Reports | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Geographically weighted temporally correlated logistic regression model | - |
dc.type | Article | - |
dc.identifier.email | Lam, KF: hrntlkf@hkucc.hku.hk | - |
dc.identifier.email | Wu, JTK: joewu@hku.hk | - |
dc.identifier.email | Lam, TY: ttylam@hku.hk | - |
dc.identifier.authority | Lam, KF=rp00718 | - |
dc.identifier.authority | Wu, JTK=rp00517 | - |
dc.identifier.authority | Lam, TY=rp01733 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1038/s41598-018-19772-6 | - |
dc.identifier.pmid | 29362396 | - |
dc.identifier.scopus | eid_2-s2.0-85041022139 | - |
dc.identifier.hkuros | 293171 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | article no. 1417(2018) | - |
dc.identifier.epage | article no. 1417(2018) | - |
dc.identifier.isi | WOS:000423044400033 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 2045-2322 | - |