Large-scale Multiple Testing: Fundamental Limits of False Discovery Rate Control and Compound Oracle
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The false discovery rate (FDR) and the false non-discovery rate (FNR), defined as the expected false discovery proportion (FDP) and the false non-discovery proportion (FNP), are the most popular benchmarks for multiple testing. Despite the theoretical and algorithmic advances in recent years, the optimal tradeoff between the FDR and the FNR has been largely unknown except for certain restricted class of decision rules, e.g., separable rules, or for other performance metrics, e.g., the marginal FDR and the marginal FNR (mFDR and mFNR). In this paper we determine the asymptotically optimal FDR-FNR tradeoff under the two-group random mixture model when the number of hypotheses tends to infinity. Distinct from the optimal mFDR-mFNR tradeoff, which is achieved by separable decision rules, the optimal FDR-FNR tradeoff requires compound rules and randomization even in the large-sample limit. A data-driven version of the oracle rule is proposed and shown to outperform existing methodologies on simulated data for models as simple as the normal mean model. Finally, to address the limitation of the FDR and FNR which only control the expectations but not the fluctuations of the FDP and FNP, we also determine the optimal tradeoff when the FDP and FNP are controlled with high probability and show it coincides with that of the mFDR and the mFNR.
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