An extended sequential goodness-of-fit multiple testing method for discrete data.
The sequential goodness-of-fit (SGoF) multiple testing method has recently been proposed as an alternative to the familywise error rate- and the false discovery rate-controlling procedures in high-dimensional problems. For discrete data, the SGoF method may be very conservative. In this paper, we introduce an alternative SGoF-type procedure that takes into account the discreteness of the test statistics. Like the original SGoF, our new method provides weak control of the false discovery rate/familywise error rate but attains false discovery rate levels closer to the desired nominal level, and thus it is more powerful. We study the performance of this method in a simulation study and illustrate its application to a real pharmacovigilance data set.
1 Faculty of Economics, SiDOR Research Group & Centro de Investigaciones Biomédicas (CINBIO), University of Vigo, Spain.
Irene
I
Castro-Conde
2 CCSOR and Faculty of Mathematics and Science, University of Applied Sciences Darmstadt, Germany.
Sebastian
S
Döhler
3 Faculty of Economics, Department of Statistics and Operations Research & Centro de Investigaciones Biomédicas (CINBIO), University of Vigo, Spain.
Jacobo
J
de Uña-Álvarez
D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D013223:Statistics as Topic
Multiple test procedures; discrete test statistics; false discovery rate; familywise error rate; sequential goodness-of-fit