
Address: Benjamin Kedem, Professor Tel: 3014055112 Department of Mathematics Fax: 3013140827 University of Maryland College Park, Maryland, 207424015
Affiliated with the Institute for Systems Research, ISR University Of Maryland, College Park. See http://www.isr.umd.edu/People/faculty/Kedem.html. For ISR reports and theses click here.
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Research Selected Publications RIT Dissertations Random Field Generation Teaching Service Information SCMA 2005The Impact of the Internet on Our Society:
Economic Impact Social ImpactI have done work in time series analysis, spacetime statistical problems, and combination of information from several sources. Some typical terms and relevant references are as follows.
Houston, TX, 01221998  Melbourne, FL, 08041998 
A stochastic color map representing a scaled clipped (6 levels) version of the 256 by 256 realization on the left. Every category is represented by a color. The 7 colors are magenta, cyan, dark blue, yellow, light green, forest green, and orange. For more information about the online generation of similar images, click on any of them. Estimation of correlation parameters in the original Gaussian random field from clipped images is treated in Kozintsev (1999). 
Student  Thesis title  Year  

George Reed  Applications of Higher Order Crossings  1983  
Donald E.K. Martin  Estimation of the Minimal Period of Periodically Correlated Sequences  1990  
Silvia R.C. Lopes  Spectral Analysis in Frequency Modulated Models  1991  
Haralabos Pavlopoulos  Statistical Inference for Optimal Thresholds  1991  
James Troendle  An Iterative Filtering Method of Frequency Detection in a Mixed Spectrum Model  1991  
TaHsin Li  Estimation of Multiple Sinusoids by Parametric Filtering  1992  
John Barnett  ZeroCrossing Rates of Some NonGaussian Processes with Application to Detection and Estimation  1996  
Konstantinos Fokianos  Categorical Time Series: Prediction and Control  1996  
Victor De Oliveira  Prediction in Some Classes of NonGaussian Random Fields  1997  
Neal Jeffries  Logistic Mixtures of Generalized Linear Model Times Series  1998  
Boris Kozintsev  Computations With Gaussian Random Fields  1999  
Richard Gagnon  Certain Computational Aspects of Power Efficiency and of State Space Models  2005  
Haiming Guo  Generalized Volatility Model and Calculating VaR Using a New Semiparametric Model  2005  
Guanhua Lu  Asymptotic Theory for MultipleSample Semiparametric Density Ratio Models and its Application to Mortality Forecasting  2007  
Shihua Wen  SemiParametric Cluster Detection  2007 
STAT 730: Time Series Analysis
MWF 2:00 PM
Syllabus
A time series is a sequence of observations made on a stochastic process sequentially in ``time''. Examples are economic time series, electronic signals (analog and digital), geophysical time series, random binary sequences, random vibration signals in mechanical systems, and many many more. Time series analysis is concerned with statistical/probabilistic methods for making inference based on sequential data; for example forecasting or prediction and interpolation. Time series analysis is widespread throughout the physical and social sciences, and engineering. Special emphasis will be placed on the use of the Splus software in time series analysis.
A related area is the analysis of spatial data, for example, geophysical 3D realizations. Prediction or interpolation in spatial data is closely related to that of time series, and in fact the two are almost indistinguishable. More generally, the correlation and spectral theory of stationary spatial data is essentially the same as that of time series.
In recent years there is a growing interest in regression models for time series tailored after ordinary linear models, except that the data are dependent, a fact that must be taken into account. Interestingly, the fact that time series are sequential is the basic fact that mitigates the problem of dependence under certain regularity conditions.
For over 40 years state space models have been playing an important role in modeling and forecasting nonstationary time series that follow certain dynamic equations. The use of state space models is widespread in such diverse fields as aerospace engineering and economics. State space models may be viewed as regression models with random coefficients.
A typical time series displays up and down oscillation. The study of this oscillation can be approached in several ways. In stationary time series this can be accomplished via spectral analysis, and higher order crossings. The expected number of level crossings by a stationary time series is an important problem in structural engineering. The course will cover current topics in time series analysis including:
STAT 740: Linear Models I
Math Building Room 0302, MWF 11:00 AM
Syllabus
Linear Models I deals with regression models, linear and nonlinear, satisfying various assumptions. The basic idea is to relate a dependent variable to a set of covariates. The course will emphasize the following topics.
STAT 741: Linear Models II
Math Building Room 1313, MWF 10:00 AM