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arXiv · 2010.08082

Nonparametric iterated-logarithm extensions of the sequential generalized likelihood ratio test

Jaehyeok Shin·Aaditya Ramdas·Alessandro Rinaldo
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Abstract

We develop a nonparametric extension of the sequential generalized likelihood ratio (GLR) test and corresponding time-uniform confidence sequences for the mean of a univariate distribution. By utilizing a geometric interpretation of the GLR statistic, we derive a simple analytic upper bound on the probability that it exceeds any prespecified boundary; these are intractable to approximate via simulations due to infinite horizon of the tests and the composite nonparametric nulls under consideration. Using time-uniform boundary-crossing inequalities, we carry out a unified nonasymptotic analysis of expected sample sizes of one-sided and open-ended tests over nonparametric classes of distributions (including sub-Gaussian, sub-exponential, sub-gamma, and exponential families). Finally, we present a flexible and practical method to construct time-uniform confidence sequences that are easily tunable to be uniformly close to the pointwise Chernoff bound over any target time interval.

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Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo. 2020-10-16. Nonparametric iterated-logarithm extensions of the sequential generalized likelihood ratio test. https://arxiv.org/abs/2010.08082

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