Open Access
2020 Is distribution-free inference possible for binary regression?
Rina Foygel Barber
Electron. J. Statist. 14(2): 3487-3524 (2020). DOI: 10.1214/20-EJS1749


For a regression problem with a binary label response, we examine the problem of constructing confidence intervals for the label probability conditional on the features. In a setting where we do not have any information about the underlying distribution, we would ideally like to provide confidence intervals that are distribution-free—that is, valid with no assumptions on the distribution of the data. Our results establish an explicit lower bound on the length of any distribution-free confidence interval, and construct a procedure that can approximately achieve this length. In particular, this lower bound is independent of the sample size and holds for all distributions with no point masses, meaning that it is not possible for any distribution-free procedure to be adaptive with respect to any type of special structure in the distribution.


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Rina Foygel Barber. "Is distribution-free inference possible for binary regression?." Electron. J. Statist. 14 (2) 3487 - 3524, 2020.


Received: 1 May 2020; Published: 2020
First available in Project Euclid: 26 September 2020

zbMATH: 07270269
MathSciNet: MR4154845
Digital Object Identifier: 10.1214/20-EJS1749

Primary: 62F35 , 62G08 , 62G15

Keywords: adaptive inference , binary regression , distribution-free , nonparametric inference

Vol.14 • No. 2 • 2020
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