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Title: Familywise Error Rate Control by Interactive Unmasking
We propose a method for multiple hypothesis testing with familywise error rate (FWER) control, called the i-FWER test. Most testing methods are predefined algorithms that do not allow modifications after observing the data. However, in practice, analysts tend to choose a promising algorithm after observing the data; unfortunately, this violates the validity of the conclusion. The i-FWER test allows much flexibility: a human (or a computer program acting on the human’s behalf) may adaptively guide the algorithm in a data-dependent manner. We prove that our test controls FWER if the analysts adhere to a particular protocol of masking and unmasking. We demonstrate via numerical experiments the power of our test under structured non-nulls, and then explore new forms of masking.  more » « less
Award ID(s):
1945266
PAR ID:
10251942
Author(s) / Creator(s):
; ;
Publisher / Repository:
PMLR (JMLR W&CP)
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
119
ISSN:
2640-3498
Page Range / eLocation ID:
2720-2729
Format(s):
Medium: X
Location:
International Conference on Machine Learning
Sponsoring Org:
National Science Foundation
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