Activity Number:
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14
- Data Science with Semiparametric Bayes
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Type:
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Invited
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Date/Time:
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Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #322198
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View Presentation
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Title:
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Semiparametric Inference for the Means of Heavy-Tailed Distributions
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Author(s):
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Hedibert Lopes* and Matt Taddy and Matt Gardner
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Companies:
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Insper and University of Chicago Booth School of Business and eBay
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Keywords:
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generalized pareto distribution ;
MCMC ;
Dirichlet process ;
A/B experiments ;
Average Treatment Effect ;
Big Data
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Abstract:
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We have a tough inference problem involving heavy tailed data. The goal is to provide a gold-standard Bayesian analysis for this problem, with nonparametric inference for the bulk of the distribution combined with parametric models - motivated from extreme value theory - in the tail. This gold standard analysis is then used to evaluate common decision rules and strategies for variance reduction (such as winsorization). The work is motivated and illustrated by a set of 72 A/B experiments on ebay.com. Our contributions include this practical semi-parametric Bayesian modelling scheme, as well as a new independence Metropolis Hastings algorithm that samples from the distribution for tail parameters through small adjustments to a parametric bootstrap distribution. Finally, we extend our analysis in a hierarchical model that shrinks tails across treatment groups to an overall background tail; this takes advantage of prior beliefs that the treatments we apply have little effect in the tail.
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Authors who are presenting talks have a * after their name.