Academic Journal

Bayesian Multivariate Nonlinear State Space Copula Models

التفاصيل البيبلوغرافية
العنوان: Bayesian Multivariate Nonlinear State Space Copula Models
المؤلفون: Kreuzer, A., Dalla Valle, L. and Czado, C.
المساهمون: Professur für Angewandte Mathematische Statistik
سنة النشر: 2019
المجموعة: Munich University of Technology (TUM): mediaTUM
مصطلحات موضوعية: info:eu-repo/classification/ddc/510, Mathematik, Time Series, Bayesian Inference, Hamiltonian Monte Carlo, Vine Copulas
الوصف: 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.
نوع الوثيقة: article in journal/newspaper
وصف الملف: application/pdf; Text
اللغة: English
Relation: https://mediatum.ub.tum.de/1523799; https://mediatum.ub.tum.de/doc/1523799/document.pdf
الاتاحة: https://mediatum.ub.tum.de/1523799
https://mediatum.ub.tum.de/doc/1523799/document.pdf
Rights: info:eu-repo/semantics/openAccess
رقم الانضمام: edsbas.3B480E9
قاعدة البيانات: BASE