Last updated: 2019-08-28
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Knit directory: stats-topics/
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Multiple testing
problem:There are a list of null hypothesis \(H_{o1},H_{o2},\dots,H_{on}\), and the p-values for each hypothesis are \(p_1,p_2,\dots,p_n\).
A Testing procedure
is a function mapping \(\{p_1,\dots,p_n\}\) to a subset of \(\{1,2,\dots,n\}\) (discoveries/rejections).
false positives
:FamilyWise Error Rate (FWER): Bound Pr(Any false discoveries)
Bound Pr(More than …% false discoveries)
Bound Pr(Number of false discoveries)
Bound Pr(Proportion of false discoveries(FDP) )
Bound Pr (False discoveries rate (FDR) )
1, 4, 5 are more common (they have abbreviation!)
Notice: if \(H_{o,global}\) is true, then every \(P_i\sim unif(0,1)\) (but may not be independent)
Statistic \(= \min\{p_1,p_2,\dots,p_n\}.\)
Reject if \(\min_i \{p_i\}\leq c\), where \(c = \frac \alpha n\), \(\alpha\) is the level of test.
Proof (upperbound of type_I error): \(\mathbb P(\min_i p_i\leq c)\leq \sum_i\mathbb P(p_i\leq c)=nc\), using the property \(\mathbb P(A\cup B)\leq \mathbb P(A)+\mathbb P(B)\).
Very conservative: the bound in the proof is hard to reach.
Statistic \[F = -2\sum_i \log(p_i).\]
Under \(H_o,global\), \(p_i\sim unif (0,1)\), if additionally \(p_i\)’s are independent, then \(F\sim \chi_{2n}^2\).
Reject if \(F\geq (1-\alpha)\) -quantile of \(\chi^2_{2n}\).
Reject \(H_{o,global}\) if:
Theorem: Under \(H_{o,global}\), if \(p_i\)’s are independent. Then \(\mathbb P\)(Simes rejects at level \(\alpha\))\(=\alpha\).