Bayesian multivariate nonlinear state space copula models

Alexander Kreuzer, Luciana Dalla Valle, Claudia Czado

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A novel flexible class of multivariate nonlinear non-Gaussian state space models, based on copulas, is proposed. Specifically, it is assumed that the observation equation and the state equation are defined by copula families that are not necessarily equal. Inference is performed within the Bayesian framework, using the Hamiltonian Monte Carlo method. 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 the 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.

Original languageEnglish
Article number107820
JournalComputational Statistics and Data Analysis
Volume188
DOIs
StatePublished - Dec 2023

Keywords

  • Bayesian inference
  • Copulas
  • Hamiltonian Monte Carlo
  • State space models

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