Benjamin Kedem

Benjamin Kedem is a Professor in the Department of Mathematics at the University of Maryland, College Park, with affiliations at the Institute for Systems Research (ISR). His academic career spans several decades with significant contributions to time series analysis, spatial statistics, and statistical methodology.

Dr. Kedem's research focuses on time series analysis, space-time statistical problems, and combination of information from multiple sources. His work includes significant contributions to higher order crossings (HOC), contraction mapping methods in spectral analysis, Rice formula applications, threshold methods for rainfall estimation, partial likelihood approaches, Bayesian-transformed-Gaussian spatial prediction, and statistical data fusion. His research has practical applications in environmental statistics, meteorology, and public health.

His recent publications demonstrate a continued focus on semiparametric methods, statistical data fusion, and computational approaches to time series and spatial analysis. The 15 most recent articles span from 2017 back to 1994, showing both contemporary relevance and foundational contributions to the field of statistics.

Dr. Kedem has directed 15 PhD dissertations, with students completing between 1983 and 2007, indicating his long-standing commitment to mentoring the next generation of statisticians. His former students include George Reed, Donald E.K. Martin, Silvia R.C. Lopes, Haralabos Pavlopoulos, James Troendle, Ta-Hsin Li, John Barnett, Konstantinos Fokianos, Victor De Oliveira, Neal Jeffries, Boris Kozintsev, Richard Gagnon, Haiming Guo, Guanhua Lu, and Shihua Wen.

He has authored or co-authored several books including 'Regression Models for Time Series Analysis' (2002), 'Time Series Analysis by Higher Order Crossings' (1994), and 'Statistical Data Fusion' (2017). His teaching portfolio includes graduate courses such as STAT 730 (Time Series Analysis), STAT 740 (Linear Models I), and STAT 741 (Linear Models II).