Zvi Gilula's current field of research is categorical data analysis, with marketing applications. He was an associate editor of the Journal of the American Statistical Association from 1986 to 2006, and was an associate editor for the International Journal of Marketing Research from 2000-2007. He has been involved in methodological consulting for well-known companies such as Roche Holdings in Europe, GTE, Prudential, TNS, and Morningside Technology in China. He was also advising the Israel National Lottery for many years, and is currently supervising TV and Internet rating in Israel. Recently, Zvi Gilula is acting as the scientific advisor in the High Frequency Algo-Trading field.
He received his education from Hebrew University, where he earned a bachelor's degree, a master's degree, and a PhD, all in statistics. He is also an elected fellow of the Royal Statistical Society and the American Statistical Association.
Outside of academia, Zvi Gilula enjoys philosophy of religions, psychology of rationality, chess, and single malts.
With Mike Evans, "The Conversion of Attitudinal Scales" (2012).
With R. McCulloch "High Dimensional Data Fusion for Categorical Vaiables” (2012).
With P. Rossi and R. McCulloch, "Direct Data Fusion" (2006).
With S. Haberman and P. van der Heijden, "Probabilistic Models for Multiple Correspondence Analysis" (2005).
With S. Haberman, "The Analysis of Categorical Profiles by Informative Summaries," Sociological Methodology (2001).
For a listing of research publications please visit Zvi Gilula’s university library listing page
A Direct Approach to Data Fusion
Date Posted: Apr 22, 2008
The generic data fusion problem is to make inferences about the joint distribution of two sets of variables without any direct observations of the joint distribution. Instead, information is only available about each set separately along with some other set of common variables. The standard approach to data fusion creates a fused data set with the variables of interest and the common variables. Our approach directly estimates the joint distribution of just the variables of interest. For the case