Alpha-investing algorithms to control the false discovery rate were first formulated by Foster and Stine and have since been generalized and applied to various ...

Oct 2, 2017 · We define a new quantity called the decaying memory false discovery rate (mem-FDR) that may be more meaningful for truly temporal applications.

In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to ...

Incorporating prior and penalty weights. [Benjamini and Hochberg '97], [Genovese et al. '06] introduced penalty and prior weights for the batch setting.

We define a new quantity called the decaying memory false discovery rate (mem-FDR) that may be more meaningful for truly temporal applications.

A new quantity called the decaying memory false discovery rate (mem-FDR) is defined that may be more meaningful for truly temporal applications, ...

Oct 2, 2017 · Abstract. In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one.

This paper unifies and extends concepts related to the online false discovery rate (FDR) control. This is a recent trendy setting where null hypotheses are ...

This article proposes novel rules for false discovery rate control (FDRC) geared towards online anomaly detection in time series. Online FDRC rules allow to.

Mar 14, 2019 · The online FDR concept is based around hypothesis testing and decisions being made in a sequential manner, with the aim being to control the FDR ...