TY - JOUR
T1 - SCALABLE SEMIPARAMETRIC INFERENCE FOR THE MEANS OF HEAVY-TAILED DISTRIBUTIONS
AU - Lopes, Hedibertfreitas
AU - Taddy, Matthew
AU - Gardner, Matthew
N1 - Publisher Copyright:
© 2019 by Emerald Publishing Limited
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
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U2 - 10.1108/S0731-90532019000040B008
DO - 10.1108/S0731-90532019000040B008
M3 - Article
AN - SCOPUS:85135143232
SN - 0731-9053
VL - 40B
SP - 141
EP - 156
JO - Advances in Econometrics
JF - Advances in Econometrics
ER -