David Siegmund
American statistician
From Wikipedia, the free encyclopedia
David Oliver Siegmund (born November 15, 1941)[1] is an American statistician who has worked extensively on sequential analysis.[2]
David O. Siegmund | |
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| Born | November 15, 1941 St. Louis, Missouri, US |
| Education | Columbia University |
| Scientific career | |
| Fields | Statistics |
| Workplaces | Stanford University |
| Herbert Robbins | |
Doctoral students | |
Biography
Siegmund grew up in Webster Groves, Missouri. He received his baccalaureate degree, in mathematics, from Southern Methodist University in 1963, and a doctorate in statistics from Columbia University in 1966. His Ph.D. advisor was Herbert Robbins. After being an assistant and then a full professor at Columbia, he went to Stanford University in 1976, where he is currently a professor of statistics. He has served twice as the chair of Stanford's statistics department.[2][3] He has also held visiting positions at Hebrew University of Jerusalem, the University of Zurich, the University of Oxford, and the University of Cambridge.[2]
Work
Siegmund has written with Herbert Robbins and Yuan-Shih Chow on the theory of optimal stopping. Much of his work has been on sequential analysis, and he has also worked on the statistics of gene mapping.[2]
Awards and honors
- Guggenheim Fellowship (1974)
- Humboldt Prize (1980)
- Elected to the American Academy of Arts and Sciences (1994)
- Invited Speaker of the International Congress of Mathematicians (1998)[4]
- Elected to the National Academy of Sciences (2002)[2]
- C. R. and Bhargavi Rao Prize in Statistics (2023)[5]
Selected publications
- (with Y. S. Chow and H. Robbins) Great Expectations: The Theory of Optimal Stopping, Boston: Houghton Mifflin, 1971.
- (with Rupert Miller) Maximally Selected Chi Square Statistics, Biometrics, 38, #4 (December 1982), pp. 1011–1016.
- Sequential Analysis: Tests and Confidence Intervals, New York: Springer, 1985, ISBN 0-387-96134-8.
- (with John D. Storey and Jonathan E. Taylor) Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach, Journal of the Royal Statistical Society, Series B 66, #1 (February 2004), pp. 187–205, doi:10.1111/j.1467-9868.2004.00439.x.