[Ml-stat-talks] Fwd: [ORFE seminars] S.S. Wilks Memorial Lecture: Lawrence Brown, Today, April 13th at 5:00pm, Computer Science 104

Barbara Engelhardt bee at CS.Princeton.EDU
Mon Apr 13 10:03:00 EDT 2015


Talk of interest today.

---------- Forwarded message ----------
From: Carol Smith <carols at princeton.edu>
Date: Mon, Apr 13, 2015 at 9:53 AM
Subject: [ORFE seminars] S.S. Wilks Memorial Lecture: Lawrence Brown,
Today, April 13th at 5:00pm, Computer Science 104
To: ORFE-TALKS at princeton.edu


 ****  S.S. Wilks Memorial Lecture   ****

DATE:   Today, April 13, 2015

TIME:   5:00pm

LOCATION:   Computer Science, room 104

SPEAKER:   Lawrence D. Brown, University of Pennsylvania

TITLE:  The Expanding Realm of Statistical Shrinkage

ABSTRACT:   The broad concept of shrinkage as statistically desirable was
already present in Galton’s 1889 description of the regression effect:
“However paradoxical it may appear at first sight, it is theoretically a
necessary fact … that the Stature of the adult offspring must [on average]
be more mediocre [= closer to the mean] than the stature of their parents.”
Contemporary use of shrinkage as a general technique, not tied to linear
regression, stems from Stein’s remarkable discovery in 1956 of a better
estimator in three or more dimensions than the customary, intuitive sample
mean. Since then, concepts of shrinkage have permeated contemporary
statistical methodologies - often in the guise of random effects models,
empirical and hierarchical Bayes modeling, and via regularization and other
high-dimensional estimation techniques. I will survey some of the
justifications for shrinkage, including a version of the geometrical
argument in Stein (1956) and a version of Stigler’s (1990) argument to show
that regression is a form of shrinkage. In 1962 Stein realized that the
deservedly popular James-Stein estimator (1961) could be statistically
justified by a hierarchical Bayes argument. I will also review this
justification and discuss some more recent developments and ramifications.
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