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You are here: Home » People » Joint and Adjunct Faculty » Hurd, Harry L.
Harry L. Hurd
 
Adjunct Professor
 
 
Office:103 Hanes Hall
Phone:(919) 962-1078
Email:
hurd(at)stat.unc.edu
 
Links:http://www.stat.unc.edu/faculty/hurd.html
 
Education:

B.S. (1963), M.S. (1964), University of Kentucky
Ph.D. (1970) Duke University
 
Professional Background:


Bell Telephone Laboratories (1964-76)

Xybion Corporation (1976-83)

Harry L. Hurd Associates (1983-)

UNC-Chapel Hill (1994- ).

 
Selected Publication:
View the complete list at http://www.stat.unc.edu/hurdpubs.html
 
More Information:

Research Interests

Hurd's primary interest is nonstationary random processes with emphasis on the periodically and almost periodically correlated processes. Periodically correlated processes are also called cyclostationary. Processes with this structure typically appear whenever systems that would otherwise generate stationary random processes are perturbed periodically with respect to time. Natural examples occur in meteorology, physiology, astronomy, mechanical systems, and in many animal sounds including voiced human speech. An Introduction to Periodically Correlated (Cyclostationary) Sequences gives a more precise definition of these processes, along with some simple mathematical models for generating them. Also included are some real world examples, some results of simultaions and a brief historical review. His theoretical interests include Fourier theory of correlation; the role of unitary operators and process representation; harmonizable processes; nonstationary random fields; prediction theory; generalized harmonic analysis; periodically perturbed dynamical systems.

Hurd's application-oriented interests are primarily focused on issues of time-series analysis for the aforementioned nonstationary process. These include estimation of the family of coefficient functions describing the correlation; estimation of the corresponding spectral densities; testing a time series for the presence of periodic correlation; statistical inference in the presence of nonstationary measurement errors; modeling by parametric systems (such as ARMA) with periodically time-varying coefficients.

 
Curriculum Vitae:
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