Degrees

• Ph.D. University of Maryland
• M.S. Universidad Simon Bolivar
• B.S. Universidad Simon Bolivar

About

Personal Faculty Website

Dr. Victor De Oliveira is a professor in the Department of Management Science and Statistics in the Carlos Alvarez College of Business. He joined the UTSA faculty in 2006 and previously worked at the University of Arkansas and Simon Bolivar University. He holds a Ph.D. in statistics from the University of Maryland, and a master’s in water resources and a bachelor’s in mathematics from the Universidad Simon Bolivar. He teaches a variety of undergraduate and graduate courses in Statistics and Applied Probability.

Research Interests

• Bayesian methods
• Environmental statistics, geostatistics,
• Markov random fields
• Spatial prediction, space-time modeling

Selected Publications

• “The Association Between Geometry and Wall Stress in Emergently Repaired Abdominal Aortic Aneurysms,” Annals of Biomedical Engineering, Vol. 45, 2017, pp. 1908-1916.
• “Statistical Data Fusion,” New Jersey: World Scientific, 2017, 200 pages.
• “On the Correlation Structure of Gaussian Copula Models for Geostatistical Count Data,” (with Z. Hahn), Australian and New Zealand Journal of Statistics, Vol. 58, 2016, pp. 47-69.
• “Prediction Intervals for Integrals of Gaussian Random Fields,” (with B. Kone), Computational Statistics and Data Analysis, Vol. 83, 2015, pp. 37-51.
• “geoCount: An R Package for the Analysis of Geostatistical Count Data,” (with L. Jing), Journal of Statistical Software, Vol. 63, No. 11, 2015, pp. 1-33.
• “Hierarchical Poisson Models for Spatial Count Data,” Journal of Multivariate Analysis, Vol. 122, 2013, pp. 393-408.
• “Bayesian Analysis of Conditional Autoregressive Models,” Annals of the Institute of Statistical Mathematics, Vol. 64, 2012, pp. 107-133.
• “Objective Bayesian Analysis of Spatial Data with Measurement Error,” The Canadian Journal of Statistics, Vol. 35, 2007, pp. 283-301.
• “On Optimal Point and Block Prediction in Log-Gaussian Random Fields,” Scandinavian Journal of Statistics, Vol. 33, 2006, pp. 523-540.
• “Objective Bayesian Analysis of Spatially Correlated Data,” with J.O. Berger and B. Sanso, Journal of the American Statistical Association, Vol. 96, 2001, pp. 1361-1374.
• “Bayesian Prediction of Clipped Gaussian Random Fields,” Computational Statistics & Data Analysis, Vol. 34, 2000, pp. 299-314.
• “Bayesian Prediction of Transformed Gaussian Random Fields,” with B. Kedem and D.A. Short, Journal of the American Statistical Association, Vol. 92, 1997, pp. 1422-1433.