About: False coverage rate

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In statistics, a false coverage rate (FCR) is the average rate of false coverage, 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 false discovery rate (FDR). Both methods address the problem of multiple comparisons, FCR from confidence intervals (CIs) and FDR from P-value's point of view. The FCR was first introduced by in his PhD thesis in 2001.

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rdfs:label	False coverage rate (en)
rdfs:comment	In statistics, a false coverage rate (FCR) is the average rate of false coverage, 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 false discovery rate (FDR). Both methods address the problem of multiple comparisons, FCR from confidence intervals (CIs) and FDR from P-value's point of view. The FCR was first introduced by in his PhD thesis in 2001. (en)
rdfs:seeAlso	False discovery rate
dct:subject	Statistical hypothesis testing Multiple comparisons
Wikipage page ID	36643952 (xsd:integer)
Wikipage revision ID	987074314 (xsd:integer)
Link from a Wikipage to another Wikipage	Statistical hypothesis testing Conditional probability Confidence interval Statistics Statistical parameter Microarray False discovery rate False positive rate Selection bias Multiple comparisons Post-hoc analysis P-values Coverage (statistics) Problem of multiple comparisons dbr:Daniel_Yekutieli
Link from a Wikipage to an external page	http://astro.temple.edu/~zhaozhg/FCR_JRSS.pdf
sameAs	False coverage rate False coverage rate False coverage rate False coverage rate
dbp:wikiPageUsesTemplate	dbt:Citation_needed dbt:Cite_journal dbt:Dead_link dbt:Main dbt:Reflist dbt:See_also dbt:Classification_of_multiple_hypothesis_tests dbt:Broader
bot	InternetArchiveBot (en)
date	December 2016 (en)
fix-attempted	yes (en)
has abstract	In statistics, a false coverage rate (FCR) is the average rate of false coverage, 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 false discovery rate (FDR). Both methods address the problem of multiple comparisons, FCR from confidence intervals (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 microarray experiments and other modern applications, there are a huge number of parameters, often tens of thousands or more and it is very important to choose the most powerful procedure. The FCR was first introduced by in his PhD thesis in 2001. (en)
gold:hypernym	Rate
prov:wasDerivedFrom	wikipedia-en:False_coverage_rate?oldid=987074314&ns=0
page length (characters) of wiki page	7674 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:False_coverage_rate
is rdfs:seeAlso of	Family-wise error rate False discovery rate Multiple comparisons problem
is Link from a Wikipage to another Wikipage of	FCR False discovery rate False positive rate Coverage probability Multiple comparisons problem
is Wikipage disambiguates of	FCR
is foaf:primaryTopic of	wikipedia-en:False_coverage_rate

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