Abstract:
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In large scale multiple testing problems in applications such as stream data, statistical process control, etc., the underlying process is regularly monitored and it is desired to control False Discovery Rate (FDR) while making real time decisions about the process being out of control or not. The existing step-wise FDR controlling procedures, such as the Benjamini-Hochberg procedure, are not applicable here because of the implicit assumption that all the p-values are available for applying the testing procedure. In this research, powerful Fallback-type procedures are developed under various dependencies for controlling FDR that award the critical constants on rejection of a hypothesis. These procedures overcome the drawback of the conventional FDR controlling procedures by making real-time decisions based on partial information available when a hypothesis is tested and allowing testing of each a-priori ordered hypothesis. Simulation studies demonstrate the effectiveness of these procedures in terms of FDR control and average power.
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