Scalable Semiparametric Inference for the Means of Heavy-tailed Distributions
Hedibert Freitas Lopes,
Matthew Taddy and
Matthew Gardner
A chapter in Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B, 2019, vol. 40B, pp 141-156 from Emerald Publishing Ltd
Abstract:
Abstract Heavy-tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This chapter outlines a procedure for inference about the mean of a (possibly conditional) heavy-tailed distribution that combines nonparametric analysis for the bulk of the support with Bayesian parametric modeling – motivated from extreme value theory – for the heavy tail. The procedure is fast and massively scalable. The work should find application in settings wherever correct inference is important and reward tails are heavy; we illustrate the framework in causal inference for A/B experiments involving hundreds of millions of users of eBay.com.
Date: 2019
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://www.emeraldinsight.com/10.1108/S0731-905320 ... RePEc&WT.mc_id=RePEc (text/html)
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532019000040b008
Ordering information: This item can be ordered from
Emerald Group Publishing, Howard House, Wagon Lane, Bingley, BD16 1WA, UK
http://www.emeraldgr ... ies.htm?id=0731-9053
Access Statistics for this chapter
More chapters in Advances in Econometrics from Emerald Publishing Ltd
Bibliographic data for series maintained by Charlotte Maiorana ().