Research Seminar Series in Statistics and Mathematics

Wirtschaftsuniversität Wien, Departments 4 D4.4.00809:00 - 10:10

Art Vortrag/Diskussion
Vortragende/rClara Grazian (Nuffield Department of Medicine, University of Oxford)
Veranstalter Institut für Statistik und Mathematik
Kontakt katrin.artner@wu.ac.at

Clara Grazian (Nuffield Department of Medicine, University of Oxford) about "Bayesian analysis of semiparametric copula models"

The Institute for Statistics and Mathematics (Department of Finance, Accounting and Statistics) cordially invites everyone interested to attend the talks in our Research Seminar Series, where internationally renowned scholars from leading universities present and discuss their (working) papers.

The list of talks for the winter term 2018/19 is available via the following link: https://www.wu.ac.at/en/statmath/resseminar

Approximate Bayesian computation (ABC) is a recent class of algorithms which allows for managing complex models where the likelihood function may be considered intractable. Complex models are usually characterized by a dependence structure difficult to model with standard tools. Copula models have been introduced as a probabilistic way to describe general multivariate distributions by considering the marginal distributions and a copula function which captures the dependence structure among the components of the vector. While it is often straightforward producing reliable estimates of the marginals, estimating the dependence structure is more complicated, in particular in high dimensional problems. Major areas of application include econometrics, engineering, biomedical science, signal processing and finance.
We consider the general problem of estimating some specific quantities of interest of a generic copula (such as, for example, tail dependence index or the Spearman's coefficient) by adopting an approximate Bayesian approach based on computing the empirical likelihood as an approximation of the likelihood function for the quantity of interest.
The approach is general, in the sense that it could be adapted both to parametric and nonparametric modelling of the marginal distributions and on a parametric or semiparametric estimation of the copula function. We will show how the Bayesian procedure based on ABC shows better properties that the classical inferential solution available in the literature and apply the method in both simulated and real examples.

Kindly note that on November 16 two talks are scheduled at our institute:
9:00 – 10:10  Clara Grazian (University of Oxford)
11:00 – 12:10  Christa Cuchiero (University of Vienna)

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