Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams
CoRR(2024)
摘要
We propose a novel nonparametric sequential test for composite hypotheses for
means of multiple data streams. Our proposed method, peeking with
expectation-based averaged capital (PEAK), builds upon the testing-as-betting
framework and provides a non-asymptotic α-level test across any stopping
time. PEAK is computationally tractable and efficiently rejects hypotheses that
are incorrect across all potential distributions that satisfy our nonparametric
assumption, enabling joint composite hypothesis testing on multiple streams of
data. We numerically validate our theoretical findings under the best arm
identification and threshold identification in the bandit setting, illustrating
the computational efficiency of our method against state-of-the-art testing
methods.
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