ArticleScientific reports2018
Geographically weighted temporally correlated logistic regression model.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Investigating the Association Between Volatile Organic Compounds and Preterm Birth in Detroit, Michigan: Protocol for the Center for Leadership in Environmental Awareness and Research Birth Cohort Study.JMIR research protocols · 2026Article
- Cross-sectional study on the relationship between physiologic and mental health status and hypertension risk among oil workers in Xinjiang.Frontiers in cardiovascular medicine · 2025Article
- Article
- Spatial multilevel analysis of age at death of under-5 children and associated determinants: EDHS 2000-2016.BMJ open · 2023Article
- CLUMondo-BNU for simulating land system changes based on many-to-many demand-supply relationships with adaptive conversion orders.Scientific reports · 2023Article
- Spatiotemporal distribution and bivariate binary analysis of antenatal and delivery care utilizations in Ethiopia: EDHS 2000-2016.BMC public health · 2023Article
- Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework.Bayesian analysis · 2023Article
- Stochastic approximation cut algorithm for inference in modularized Bayesian models.Statistics and computing · 2021Article
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4 authors.
Funding
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.
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