I continue to work as a Senior Statistical Programmer/Analyst at Duke Clinical Research Institute, in Durham, North Carolina. This past year, my primary focus has been clinical trials and manuscript work over many different therapeutic areas.
After 41 years in higher education, I retired from my position as CIO for the University of Central Missouri in 2010 where I had served since 1996. This was after holding teaching and administrative positions at 6 other institutions including my undergraduate institution, the University of Arkansas, along with The Medical College of Wisconsin and the statewide higher education system of South Dakota. The PhD from the UGA has allowed me to pursue my many interests throughout my career, both in teaching(statistics, mathematics, and computer science) and administrative roles in both the ac
I am currently completing an MS in Forestry with an emphasis on forest biometrics.
Jessi works as a risk analyst for First Data and loves her job! She is also serving as the president of the Houston, TX UGA alumni chapter.
I have been working odd jobs for the past year saving up money. I am applying to MBA programs starting in the fall.
I am currently working as a Senior Operations Analyst in Business Analytics for Aflac. My degree has afforded me the opportunity to work as an Affirmative Action/EEO Specialist as well as my current position.
I am the chair of Information Systems and Operations Management but will step down on July 1.
Currently pursuing a joint JD/MBA at Georgia State University.
Modern graphical tools have enhanced our ability to learn many things from data directly. In recent years, dimension reduction has proven to be an effective tool for generating lo dimensional summary plots without appreciable loss of information. Some well-known inverse regression methods for dimension reduction such as SIR (Li, 1991) and SAVE (Cook & Weisberg, 1991) have been developed to estimate summary plots for regression and discriminant analysis. In this article, we suggest a new method (SAT) that makes use of inverse third moments.
Canonical correlation analysis (Hotelling, 1935, 1936) has been long used as a standard method in multivariate analysis. Whose merit is simply to catch the linear relations between p x 1 vector Y-set and q x 1 vector X-set. However, it could fail when there is no linear trend between them while the relation goes through nonlinear way. To overcome this drawback, here we develop a new canonical correlation method between Y-set and X-set, called moment canonical correlation.