Date of Award
Doctor of Philosophy
Elliptical copulas provide flexibility in modeling the dependence structure of a random vector. They are often parameterized with a correlation matrix and a scalar function, called generator. The estimation of the generator can be challenging, because it is a functional parameter. In this dissertation, we provide a rigorous approach to estimating the generator in a Bayesian framework, which is simpler, more robust, and outperforms existing estimation methods in the literature. Based on the proposed framework in this dissertation, other researchers may modify the model for other types of generators in their own research.
Recieved from ProQuest
Liang, Panfeng, "Nonparametric Estimation of Elliptical Copulas" (2023). Open Access Theses & Dissertations. 3815.