Vorlesen

Research Seminar Series in Statistics and Mathematics

Wirtschaftsuniversität Wien , Departments 4 D4.4.008 09:00 - 10:30

Art Vortrag/Diskussion
SpracheEnglish
Vortragende/rNadja Klein (School of Business and Economics, Humboldt-Universität zu Berlin)
Veranstalter Institut für Statistik und Mathematik
Kontakt katrin.artner@wu.ac.at

Nadja Klein (School of Business and Economics, Humboldt-Universität zu Berlin) about "Implicit Copulas from Bayesian Regularized Regression Smoothers"

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.
No registration required.

The list of talks for the summer term 2019 is available via the following link: https://www.wu.ac.at/en/statmath/resseminar

Abstract:
We show how to extract the implicit copula of a response vector from a Bayesian regularized regression smoother with Gaussian disturbances. The copula can be used to compare smoothers that employ different shrinkage priors and function bases. We illustrate with three popular choices of shrinkage priors – a pairwise prior, the horseshoe prior and a g prior augmented with a point mass as employed for Bayesian variable selection – and both univariate and multivariate function bases. The implicit copulas are high-dimensional, have flexible dependence structures that are far from that of a Gaussian copula, and are unavailable in closed form. However, we show how they can be evaluated by first constructing a Gaussian copula conditional on the regularization parameters, and then integrating over these. Combined with non-parametric margins the regularized smoothers can be used to model the distribution of non-Gaussian univariate responses conditional on the covariates. Efficient Markov chain Monte Carlo schemes for evaluating the copula are given for this case. Using both simulated and real data, we show how such copula smoothing models can improve the quality of resulting function estimates and predictive distributions.



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