Show simple item record

dc.contributor.authorKreuzer, Aen
dc.contributor.authorValle, LDen
dc.contributor.authorCzado, Cen
dc.date.accessioned2019-11-15T10:52:34Z
dc.date.available2019-11-15T10:52:34Z
dc.identifier.urihttp://hdl.handle.net/10026.1/15148
dc.description.abstract

In this paper we propose a flexible class of multivariate nonlinear non-Gaussian state space models, based on copulas. More precisely, we assume that the observation equation and the state equation are defined by copula families that are not necessarily equal. For each time point, the resulting model can be described by a C-vine copula truncated after the first tree, where the root node is represented by the latent state. Inference is performed within the Bayesian framework, using the Hamiltonian Monte Carlo method, where a further D-vine truncated after the first tree is used as prior distribution to capture the temporal dependence in the latent states. Simulation studies show that the proposed copula-based approach is extremely flexible, since it is able to describe a wide range of dependence structures and, at the same time, allows us to deal with missing data. The application to atmospheric pollutant measurement data shows that our approach is suitable for accurate modeling and prediction of data dynamics in the presence of missing values. Comparison to a Gaussian linear state space model and to Bayesian additive regression trees shows the superior performance of the proposed model with respect to predictive accuracy.

en
dc.language.isoenen
dc.subjectstat.MEen
dc.subjectstat.MEen
dc.titleBayesian Multivariate Nonlinear State Space Copula Modelsen
dc.typeJournal Article
plymouth.author-urlhttp://arxiv.org/abs/1911.00448v1en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
dc.rights.embargoperiodNot knownen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeJournal Article/Reviewen


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record


All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
@mire NV