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- Title
Pair-copula constructions for non-Gaussian DAG models.
- Authors
Bauer, Alexander; Czado, Claudia; Klein, Thomas
- Abstract
We propose a new type of multivariate statistical model that permits non-Gaussian distributions as well as the inclusion of conditional independence assumptions specified by a directed acyclic graph. These models feature a specific factorisation of the likelihood that is based on pair-copula constructions and hence involves only univariate distributions and bivariate copulas, of which some may be conditional. We demonstrate maximum-likelihood estimation of the parameters of such models and compare them to various competing models from the literature. A simulation study investigates the effects of model misspecification and highlights the need for non-Gaussian conditional independence models. The proposed methods are finally applied to modeling financial return data. The Canadian Journal of Statistics 40: 86-109; 2012 © 2012 Statistical Society of Canada
- Subjects
MULTIVARIATE analysis; STATISTICS; GAUSSIAN distribution; DISTRIBUTION (Probability theory); ACYCLIC model
- Publication
Canadian Journal of Statistics, 2012, Vol 40, Issue 1, p86
- ISSN
0319-5724
- Publication type
Article
- DOI
10.1002/cjs.10131