Problem
Research questions and friction points this paper is trying to address.
federated multiple testing
family-wise error control
statistical power loss
Report resolution in federated multiple testing under family-wise error control
๐ค AI Summary
็ ็ฉถ่งฃๅณไบๅคๆบๆๅจ่ๅๅค้ๆฃ้ชไธญๅ ๆฅๅๅ่พจ็ๅ้ๅฏผ่ด็็ป่ฎกๅๆๆๅคฑ้ฎ้ข๏ผ้่ฟไผๅๆฅๅๅๅๅนถ่งๅๆฅๅๅฐ่ฟ็งๆๅคฑใ
๐ Abstract
Several institutions test one family of hypotheses under family-wise error control but cannot pool their data, so each site releases, for each hypothesis, only a report of its own p-value. The report's resolution is the number of values it can take. We quantify the power lost to such reports relative to the most powerful centralized procedure (the oracle), with reports and combining rule chosen optimally. A report from which the p-value can be recovered loses no power, so the loss is due to compression, not decentralization. Under the stated regularity and power-objective conditions, every finite resolution loses power, and the optimal loss decays as the inverse square of the resolution: equal-width intervals attain this order, and no partition into as many cells improves it. In two-site Beta examples, optimized one-bit reports are nearly lossless for a single hypothesis but lose several times as much power for two. At level 0.05 with two Beta(1, 2) hypotheses, optimally combined three-bit equal-width reports retain 99.2% of oracle power. We give the power-optimal combining rule for given reports, and a rule using only their exactly known null distribution, valid under arbitrary dependence across hypotheses.
Innovation
Methods, ideas, or system contributions that make the work stand out.
federated multiple testing
report resolution
power loss
equal-width intervals
combining rule
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