Federated multiple testing with quantized p-values under family-wise error control

@misc{dubey2026federated,
  title = {{Federated multiple testing with quantized p-values under family-wise error control}},
  author = {Prasanjit Dubey and Xiaoming Huo},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2609.19708},
  eprint = {2609.19708},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.19708},
}

TY  - UNPB
TI  - Federated multiple testing with quantized p-values under family-wise error control
AU  - Prasanjit Dubey
AU  - Xiaoming Huo
PY  - 2026
DA  - 2026/10/09
PB  - arXiv
DO  - 10.48550/arxiv.2609.19708
UR  - https://arxiv.org/abs/2609.19708
AB  - Several institutions test one family of hypotheses under family-wise error control but cannot pool their data, so each site releases only a quantized report of its p-value for each hypothesis, such as whether the p-value is significant. 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 given all p-values (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.
ER  -