EconPapers    
Economics at your fingertips  
 

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 ().

 
Page updated 2023-01-18
Handle: RePEc:eme:aecozz:s0731-90532019000040b008