Papers › Time-uniform, nonparametric, nonasymptotic confidence sequences

Time-uniform, nonparametric, nonasymptotic confidence sequences

18 Oct 2018arXiv:1810.08240links table onlyarchive 2025-07-28

Steven R. Howard, Aaditya Ramdas, Jon McAuliffe, Jasjeet Sekhon

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A confidence sequence is a sequence of confidence intervals that is uniformly valid over an unbounded time horizon. Our work develops confidence sequences whose widths go to zero, with nonasymptotic coverage guarantees under nonparametric conditions. We draw connections between the Cram\'er-Chernoff method for exponential concentration, the law of the iterated logarithm (LIL), and the sequential probability ratio test -- our confidence sequences are time-uniform extensions of the first; provide tight, nonasymptotic characterizations of the second; and generalize the third to nonparametric settings, including sub-Gaussian and Bernstein conditions, self-normalized processes, and matrix martingales. We illustrate the generality of our proof techniques by deriving an empirical-Bernstein bound growing at a LIL rate, as well as a novel upper LIL for the maximum eigenvalue of a sum of random matrices. Finally, we apply our methods to covariance matrix estimation and to estimation of sample average treatment effect under the Neyman-Rubin potential outcomes model.

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gostevehoward/confseq officialmentioned in papermentioned on GitHub report
gostevehoward/cspaper officialmentioned in papermentioned on GitHubMIT report
gsbDBI/adaptive-confidence-intervals mentioned on GitHubGPL-3.0 report
wannabesmith/confseq mentioned on GitHubMIT report

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