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The positive false discovery rate: A Bayesian interpretation and the qvalue
 Annals of Statistics
, 2003
"... Multiple hypothesis testing is concerned with controlling the rate of false positives when testing several hypotheses simultaneously. One multiple hypothesis testing error measure is the false discovery rate (FDR), which is loosely defined to be the expected proportion of false positives among all s ..."
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Cited by 337 (8 self)
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Multiple hypothesis testing is concerned with controlling the rate of false positives when testing several hypotheses simultaneously. One multiple hypothesis testing error measure is the false discovery rate (FDR), which is loosely defined to be the expected proportion of false positives among all
Identifying differentially expressed genes using false discovery rate controlling procedures
 BIOINFORMATICS 19: 368–375
, 2003
"... Motivation: DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when th ..."
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Cited by 233 (2 self)
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Motivation: DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when
Inference for the Generalization Error
, 2001
"... In order to compare learning algorithms, experimental results reported in the machine learning literature often use statistical tests of signicance to support the claim that a new learning algorithm generalizes better. Such tests should take into account the variability due to the choice of training ..."
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Cited by 184 (3 self)
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of the variance of a crossvalidation estimator of the generalization error that takes into account the variability due to the randomness of the training set as well as test examples. Our analysis shows that all the variance estimators that are based only on the results of the crossvalidation experiment must
A Static Analyzer for Large SafetyCritical Software
, 2003
"... We show that abstract interpretationbased static program analysis can be made e#cient and precise enough to formally verify a class of properties for a family of large programs with few or no false alarms. This is achieved by refinement of a general purpose static analyzer and later adaptation to p ..."
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Cited by 271 (54 self)
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We show that abstract interpretationbased static program analysis can be made e#cient and precise enough to formally verify a class of properties for a family of large programs with few or no false alarms. This is achieved by refinement of a general purpose static analyzer and later adaptation
Compressed Bloom Filters
, 2001
"... A Bloom filter is a simple spaceefficient randomized data structure for representing a set in order to support membership queries. Although Bloom filters allow false positives, for many applications the space savings outweigh this drawback when the probability of an error is sufficiently low. We in ..."
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Cited by 255 (8 self)
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A Bloom filter is a simple spaceefficient randomized data structure for representing a set in order to support membership queries. Although Bloom filters allow false positives, for many applications the space savings outweigh this drawback when the probability of an error is sufficiently low. We
GenomeWide Detection of Alternative Splicing in Expressed Sequences Using Partial Order Multiple Sequence Alignment Graphs
 Nucleic Acids Res
, 2001
"... this paper we present a detailed examination of the technical problems we have encountered in undertaking highthroughput analyses of alternative splicing over the last four years, and the specific solutions we have developed for these problems, in seeking to minimize both false positive and false n ..."
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Cited by 223 (7 self)
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this paper we present a detailed examination of the technical problems we have encountered in undertaking highthroughput analyses of alternative splicing over the last four years, and the specific solutions we have developed for these problems, in seeking to minimize both false positive and false
False Data Injection Attacks against State Estimation in Electric Power Grids
, 2009
"... A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring ..."
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Cited by 154 (2 self)
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that these techniques can also defeat malicious measurements injected by attackers. In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks
Controlling the familywise error rate in functional neuroimaging: a comparative review
 Statistical Methods in Medical Research
, 2003
"... Functional neuroimaging data embodies a massive multiple testing problem, where 100 000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three a ..."
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Cited by 173 (7 self)
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Functional neuroimaging data embodies a massive multiple testing problem, where 100 000 correlated test statistics must be assessed. The familywise error rate, the chance of any false positives is the standard measure of Type I errors in multiple testing. In this paper we review and evaluate three
The moral problem
, 1994
"... Even with the considerable progress in atmospheric science during the twentieth century, there remains considerable room for improvement in the accuracy of the public warnings of tornadoes, flash floods, large hail and damaging thunderstorm winds. But even if we had perfect knowledge of the process ..."
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Cited by 204 (9 self)
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kinds of errors. One is when an event that is predicted does not occur, i.e., a false alarm. The second is when an event occurs but is not predicted, i.e., a surprise. There is an inevitable tradeoff between the two kinds of errors; steps taken to reduce one will increase the other. This article
Results 11  20
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3,015