Abstract Details
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Activity Number:
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180
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #308905 |
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Title:
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Estimating Average Proportional Changes in Large, Sparse Data
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Author(s):
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Ryan Giordano*+
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Companies:
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Keywords:
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Cochran-Mantel-Haenszel ;
Proportional Effects ;
Method of Moments ;
Big data ;
Internet advertising
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Abstract:
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I will present a simple, scalable method for estimating average proportional changes between the means of sparse, noisy observations. It is particularly useful in discrete cases when the presence of zeros make logarithms untenable, and the size of the data makes making multiple passes through the data slow and cumbersome. The method's simplicity and parallelizability has made it a popular method at Google for dealing with Simpson's paradox when comparing millions or billions of paired data points, each of which has relatively little (or missing) data. The estimator can be understood as a method-of-moments interpretation of the classical Cochran-Mantel-Haenszel estimator. Motivating applications from internet advertising will be discussed.
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Authors who are presenting talks have a * after their name.
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