Seminar 2021-11-05

Copula-Based Bivariate for Poisson Time Series Models

Norou Diawara
Professor, Department of Mathematics & Statistics, Old Dominion University

Date: Friday, November 5, 2021
Time: 11am–noon
Location: SUB I 3A
Live-stream: The talk will also be live-streamed via Zoom. For connection information, please contact statistics@gmu.edu.

To attend this seminar in person, please RSVP by the time of the event!

Abstract

The class of bivariate integer-valued time series models is gaining rapid popularity. However, its efficiency and adaptability are being challenged because of zero-inflation of count time series (ZITS) and algorithm techniques. In this presentation, the bivariate copula is presented with ZITS. The computational algorithm is proposed via copula theory. Each series follows a Markov chain with the serial dependence is captured using copula-based transition probabilities with Poisson and zero-inflated Poisson margins. The copula theory is also used to capture bivariate ZITS where the dependence between the two series using the bivariate Gaussian, t-copula functions. Likelihood based inference is used to estimate the models parameters for simulated and real data with the bivariate integrals of the gaussian or t copula functions being evaluated using standard randomized Monte Carlo methods.

About the speaker

Dr. Norou Diawara is Professor in the Mathematics and Statistics Department at Old Dominion University. His current research interests are on Multivariate and Functional Data Analysis, Modeling, Probability Theory and its Applications in Biostatistics and Time Series. His work is applied to Discrete Choice modeling, Spatio-temporal models and on Statistical Pattern recognition using copula. He has been collaborating with researchers in the Engineering, Health Sciences, Oceanography and Psychology. His support is in statistical design and methodology studies, size/power calculations in research activities, and has served as a collaborative investigator on applied grants, study implementation.