Date of Award
2019-01-01
Degree Name
Master of Science
Department
Computational Science
Advisor(s)
Ori . Rosen
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.
Language
en
Provenance
Received from ProQuest
Copyright Date
2019-12
File Size
51 pages
File Format
application/pdf
Rights Holder
Panfeng Liang
Recommended Citation
Liang, Panfeng, "Modeling Correlated Data via Copulas" (2019). Open Access Theses & Dissertations. 2871.
https://scholarworks.utep.edu/open_etd/2871