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dc.contributor.authorZhang, Zhongwei
dc.contributor.authorArellano-Valle, Reinaldo B.
dc.contributor.authorGenton, Marc G.
dc.contributor.authorHuser, Raphaël
dc.date.accessioned2021-01-14T10:40:41Z
dc.date.available2021-01-14T10:40:41Z
dc.date.issued2021-01-06
dc.identifier.urihttp://hdl.handle.net/10754/666901
dc.description.abstractCorrelated binary response data with covariates are ubiquitous in longitudinal or spatial studies. Among the existing statistical models the most well-known one for this type of data is the multivariate probit model, which uses a Gaussian link to model dependence at the latent level. However, a symmetric link may not be appropriate if the data are highly imbalanced. Here, we propose a multivariate skew-elliptical link model for correlated binary responses, which includes the multivariate probit model as a special case. Furthermore, we perform Bayesian inference for this new model and prove that the regression coefficients have a closed-form unified skew-elliptical posterior. The new methodology is illustrated by application to COVID-19 pandemic data from three different counties of the state of California, USA. By jointly modeling extreme spikes in weekly new cases, our results show that the spatial dependence cannot be neglected. Furthermore, the results also show that the skewed latent structure of our proposed model improves the flexibility of the multivariate probit model and provides better fit to our highly imbalanced dataset.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2101.02233
dc.rightsArchived with thanks to arXiv
dc.titleTractable Bayes of Skew-Elliptical Link Models for Correlated Binary Data
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Group
dc.contributor.departmentStatistics
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionDepartamento of Estad´ıstica, Pontificia Universidad Cat´olica de Chile, Santiago, Chile.
dc.identifier.arxivid2101.02233
kaust.personZhang, Zhongwei
kaust.personGenton, Marc G.
kaust.personHuser, Raphaël
refterms.dateFOA2021-01-14T10:41:10Z


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