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Activity Number: 275 - Advances in Dependence Modeling Through Copulas
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: SSC
Abstract #326483 Presentation
Title: Bayesian Inference for Conditional Copulas Using Gaussian Process Single Index Models
Author(s): Radu V Craiu* and Evgeny Levi
Companies: University of Toronto and University of Toronto
Keywords: Bayes Inference; Conditional Copula; Gaussian Process; Markov chain Monte Carlo; Cross-Validated Marginal ; Watanabe Information Criterion

Parametric conditional copula models allow the copula parameters to vary with a set of covariates according to an unknown calibration function. Flexible Bayesian inference for the calibration function of a bivariate conditional copula is proposed via a sparse Gaussian process (GP) prior distribution over the set of smooth calibration functions for the single index model (SIM). The estimation of parameters from the marginal distributions and the calibration function is done jointly via Markov Chain Monte Carlo sampling from the full posterior distribution. A new Conditional Cross Validated Pseudo-Marginal (CCVML) criterion is introduced in order to perform copula selection and is modified using a permutation-based procedure to assess data support for the simplifying assumption.

Authors who are presenting talks have a * after their name.

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