SCRC 2005 / FIM XII
   Hosted by Auburn University

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Regression Models for Time Series

 

Benjamin Kedem received his PhD in statistics from Carnegie Mellon in 1973. He joined the University of Maryland in 1975 where he is currently Professor in the Mathematics Department, and an affiliate of the Institute for Systems Research. His research interests are time series analysis, statistical modeling of satellite observations, and applied semiparametrics.  His research on higher order crossings (HOC) was selected as an accomplishment by Air Force Office of Scientific Research, 1986. He is the recipient of the 1988 W.R.G. Baker award given for the most outstanding paper in the IEEE journals, and the recipient of a 1997 NASA/Goddard award, for his work on rainfall measurement from space. He is a fellow of the American Statistical Association.

 

Semiparametric Time Series Prediction

Given m time series regression models, linear or not, with additive noise components, it is shown how to estimate the predictive probability distribution of all the time series conditional on the observed and covariate data at the time of prediction. This is done by a certain synergy argument, assuming that the distributions of the residual components associated with the regression models are tilted versions of a reference distribution. Point predictors are obtained from the predictive distribution as a byproduct. Applications to US mortality rates prediction and to value at risk (VaR) estimation will be discussed.

 

 

 

12th Annual Conference of the Forum for Interdisciplinary Mathematics (FIM XII)