Modeling Correlated Data via Copulas
Abstract
Copulas are widely used to model the dependency structure among components of multi- variate data sets. Elliptical copulas, such as Gaussian copula, are most popular copulas being used since many data sets follow elliptical distributions or meta-elliptical distribu- tions (Fang et al. (2002)). However, today’s approaches and software packages require us to assume the specific category, such as Gaussian or Student’s T, of the elliptical cop- ula before estimating it. In this thesis, we will propose a Bayesian method using Markov chain Monte Carlo (MCMC) methods to estimate the density function of elliptical copulas without specifying it is the copula of Gaussian, Student’s T or Logistic, etc.
Subject Area
Statistics
Recommended Citation
Liang, Panfeng, "Modeling Correlated Data via Copulas" (2019). ETD Collection for University of Texas, El Paso. AAI27671191.
https://scholarworks.utep.edu/dissertations/AAI27671191
