R. S. Wenocur

Roberta S. Wenocur is a known for employing a new method, combining more than two distinct areas of mathematics, using hyperplanes and algebraic methods to establish combinatorial results in VC-theory, probability theory, and statistics, which are now cited by Vladimir Vapnik and researchers in the fields of computer learning theory, neural nets, and bioinformatics.

The researchers used and cited the work of Wenocur (with Dudley) to develop this Surgical Procedure to help those with impaired hearing.

Also among those who have cited Wenocur are Hausler, Luc Devroye, Janos Galambos and numerous other well-known researchers.

Biography
Before earning her Ph.D. Wenocur worked in actuarial science developing software for use within the insurance industry. Wenocur went on to earn her Ph.D. in mathematics in 1979 under Janos Galambos. After completing her dissertation, Waiting Times and Return Periods Related to Order Statistics, Wenocur has served as faculty member at several universities including the University of Pennsylvania. She has also worked at NCV (New Consulting Ventures), a company that she founded in 1966, where she continues research in mathematics, statistics, and applications. Wenocur is a member of AMS, MAA, APS, and MSRI at the Fibonacci level.

Research
Wenocur’s main fields of interest are probability theory and mathematical statistics, including empirical measures, empirical processes, economic models, investment strategies, Vapnik-Cervonenkis theory, combinatorics, order statistics, Bose-Einstein statistics, probabilistic proofs of hypergeometric identities. Along with R. M. Dudley, Wenocur is known for some “Special Vapnik-Cervonenkis Classes”, which are widely cited and employed in various applications.

Wenocur has received grants from the National Science Foundation to continue her research.

*1982: Grant for research in empirical measures, empirical processes, and rates of convergence.
*1982: Grant for research related to women in science and mathematics.
*1991: Grant for research in chaos and fractal geometry.

Selected publications, book chapters, and works in progress

(1) A probabilistic proof of Gauss’s 2F1 identity . (October 1994). J. Combin. Theory, Series A, 68,
212 - 215.

(2) Order statistics and combinatorial identities. (May 1993). Extreme Value Theory and
Applications 3, 219 - 223.

(3) Predictive models of correlator/tracker algorithm performance in the presence of false alarms. Data
Fusion 2, 1989, 340-346.

(4) An analytic model for the effect of false reports on surveillance tracking. Data Fusion 1, 1988, 630 -640.

(5) Some special Vapnik-Cervonenkis classes. ( 1981, with R. M. Dudley ). Discrete Mathematics 33,
313 - 318

(6) Recurrence of a modified random walk and its application to an economic model. (1981, with S.
Salant). SIAM J. Appl. Math. 40, 163 - 166.

(7) Rediscovery t and alternate proof of Gauss's identity . (1980). Annals of Discrete Mathematics 9, 79
- 82.

(8) Waiting times and return periods related to order statistics: an application of urn models. (1981).
Statis. Distrib. in Scientific Work 6, 419 - 434 .

(9) Waiting times and return periods to exceed the maximum of a previous sample. ( 1981 ). Statis.
Distrib. in Scientific Work 6, 411 - 418 .

(10) Order statistics and an experiment in software design. (1981). Computer Science and Statistics 13,
281 - 283.

(11) Order statistics in the computer science classroom. (1981). Computer Science and Statistics 13.

(12) Bounds for the uniform deviation of empirical measures over special classes of sets. ( 1981 ).
Stoch.Proc.and Applic. 10, p. 98 .

(13) Group theory as a consequence of the theory of equations. ( 1977 ). Temple University
Publications.

(14) Series of entertaining mathematical books for children. In progress.

(15) Contagion and Bose-Einstein statistics (submitted for publication, 2008)

(16) Mathematical prodigies. In progress.

Selected Technical Reports
(content classified; titles not classified; not permitted to be published)

(a) Tests for a Downward Statistical Trend in Oil or Gas Pool Size as Discoveries are Made Through
Time.

(b) Likelihood Test of Independence.

(c) An Instantaneous Detection Probability Algorithm.

(d) A Generalized Cumulative Detection Probability Algorithm.

(e) Sensitivity of Detection Model to the Multi-Ping Criterion.

(f) Simulation Involving Passive Buoys.

(g) An Analytic Model for the Effect of False Reports on Surveillance Tracking.

(h) An Analytic Model for the Effect of False Reports on Surveillance Tracking in the Discrete Glimpse
Case.

(i) Relative Likelihood of Correlated Valid Reports Versus False Tracks.

(j) Discriminants for Time Series Analysis.

(k) Analysis of Pulse Repetition Interval and Scan Rate as Time Series.

(l) WSS Track File Analysis.

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