Charles M. Stein

Charles M. Stein
Born	March 22, 1920 Brooklyn, New York, U.S.
Died	November 24, 2016(2016-11-24) (aged 96) Fremont, California, U.S.
Alma mater	Columbia University University of Chicago
Scientific career
Fields	Statistics
Institutions	Stanford University
Doctoral advisor	Abraham Wald

Charles Max Stein (March 22, 1920 – November 24, 2016) was an American mathematical statistician and professor of statistics at Stanford University.

He received his Ph.D in 1947 at Columbia University with advisor Abraham Wald. He held faculty positions at Berkeley and the University of Chicago before moving permanently to Stanford in 1953. He is known for Stein's paradox in decision theory, which shows that ordinary least squares estimates can be uniformly improved when many parameters are estimated; for Stein's lemma, giving a formula for the covariance of one random variable with the value of a function of another when the two random variables are jointly normally distributed; and for Stein's method, a way of proving theorems such as the Central Limit Theorem that does not require the variables to be independent and identically distributed. He was a member of the National Academy of Sciences. He died in November 2016 at the age of 96.

Works

• Approximate Computation of Expectations, Institute of Mathematical Statistics, Hayward, CA, 1986.
• A bound for the error in the normal approximation to the distribution of a sum of dependent random variables, Sixth Berkeley Stanford Symposium, pages 583-602.

Interviews

• DeGroot, Morris H. (1986). "A Conversation with Charles Stein". Statistical Science. 1 (4): 454–462. doi:10.1214/ss/1177013517. JSTOR 2245793.
• "Charles Stein: The Invariant, the Direct and the "Pretentious"" (PDF). {{cite journal}}: Cite journal requires |journal= (help)

References

• Charles M. Stein at the Mathematics Genealogy Project
• National University of Singapore Program Honoring Prof. Stein
• Photograph of Stein
• Another photograph

See also

• James–Stein estimator
• Stein's lemma
• Stein's method
• Stein's unbiased risk estimate
• Stein's loss
• Stein discrepancy