False coverage rate

In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, a false coverage rate (FCR) is the average rate of false <a href="/facts/Coverage_(statistics)/Lm1rNVjb">coverage</a>, i.e. not covering the true parameters, among the selected intervals.
The FCR gives a simultaneous coverage at a (1 − α)×100% level for all of the parameters considered in the problem. The FCR has a strong connection to the <a href="/facts/False_discovery_rate/NqyJxV8s">false discovery rate</a> (FDR). Both methods address the <a href="/facts/Problem_of_multiple_comparisons/lAItaL18">problem of multiple comparisons</a>, FCR from <a href="/facts/Confidence_interval/NS8mG5UE">confidence intervals</a> (CIs) and FDR from P-value's point of view.
FCR was needed because of dangers caused by selective inference. Researchers and scientists tend to report or highlight only the portion of data that is considered significant without clearly indicating the various hypothesis that were considered. It is therefore necessary to understand how the data is falsely covered. There are many FCR procedures which can be used depending on the length of the CI – Bonferroni-selected–Bonferroni-adjusted, Adjusted BH-Selected CIs (Benjamini and Yekutieli 2005). The incentive of choosing one procedure over another is to ensure that the CI is as narrow as possible and to keep the FCR. For <a href="/facts/Microarray/LvJeNodc">microarray</a> experiments and other modern applications, there are a huge number of <a href="/facts/Statistical_parameter/fkJju25M">parameters</a>, often tens of thousands or more and it is very important to choose the most powerful procedure.
The FCR was first introduced by <a href="/facts/Daniel_Yekutieli/fgSldPu0">Daniel Yekutieli</a> in his PhD thesis in 2001.

False coverage rate open-in-new

False coverage rate