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Activity Number: 612
Type: Invited
Date/Time: Wednesday, August 7, 2013 : 4:00 PM to 5:50 PM
Sponsor: ASA
Abstract - #310413
Title: From Fisher to Big Data: Continuities and Discontinuities
Author(s): Peter Bickel*+
Companies: University of California - Berkeley
Keywords: likelihood ; decision theory ; machine learning ; computation

In two major papers in 1922 and 1925, Fisher introduced many of the ideas that, when coupled with Wald's decision theoretic point of view of 1950, underlay the structure of statistics until the 1980s.That period coincided, not accidentally, with the beginnings of the widespread introduction of computers and our ability to use them to gather and analyze "big data." I will look at how the Fisherian concepts have evolved in response to the new environment and study new ideas. Thus, I will argue that "sufficiency" has evolved to "data compression," "efficiency" has had to include computational considerations, and issues of scale-- "parameters" and procedures such as "maximum likelihood"-- have had to be considered in the context of larger semi- and nonparametric models, as in robustness. The steady rise in computational capability during the last 30-40 years has enabled implementation of the older Bayesian point of view, computer-intensive methods such as Efron's "bootstrap," and the introduction of the "machine learning" point of view and methods from computer science. I will try to support my argument from the literature, some of my own work, and my experience with ENCODE--a Big Data project in biology.

Authors who are presenting talks have a * after their name.

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