At the UTSA College of Business, we share a commitment to research and publications, which positions us well to absorb quality faculty and students. Research is part of a learning cycle, significantly contributing to the absorption of quality students and retention of quality faculty yielding an institution of higher learning that continuously strives for excellence.

UTSA College of Business faculty members are conducting research throughout a variety of disciplines and have been recognized by their peers for their accomplishments. The College of Business was recently ranked as one of the top 100 business schools nationwide according to the Business School Research Rankings compiled by the University of Texas at Dallas School of Management. The ranking is based on the research publications of business faculty members from 2009-2010.

Also, the Department of Marketing was ranked 46th nationally and the Department of Finance was ranked 89th nationally for their research publications according to UT Dallas research rankings for 2009-2011.

Statistics Faculty Members Receives $150,000 National Science Foundation Grant

Victor De Oliveira, associate professor of management science and statistics, received a three-year $150,000 grant from the National Science Foundation. De Oliveira's research interests include Bayesian methods, environmental statistics and geostatistics.

The project is "Geostatistical Modeling of Spatial Discrete Data." The research consists of three parts. First, a class of hierarchical spatial models will be developed that seeks to ameliorate some limitations of the currently used models identified by the PI. The properties of these models and likelihood based methods to fit them will be studied. Second, a class of nonhierarchical spatial models will be developed that seeks to represent a wide range of spatial discrete data, not just counts, having association structures that are complementary to those in the class of hierarchical spatial models. The properties of these models and likelihood based methods to fit them will be studied. Third, a Bayesian method to perform goodness-of-fit for the aforementioned two classes of spatial models will be developed.

The statistical methodology developed in the course of this project would have immediate methodological and practical impacts on the earth and social sciences, where spatial data are routinely collected but models and methods for their analysis are scarce. The proposed classes of models will substantially increase the arsenal of tools available to spatial data analysts and the possibility of representing a wide range of behaviors for spatial discrete data.

Nandini Kannan