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Reply: Birnbaum’s (2012) statistical tests of independence have
"... unknown Type-I error rates and do not replicate within participant ..."
A direct approach to false discovery rates
, 2002
"... Summary. Multiple-hypothesis testing involves guarding against much more complicated errors than single-hypothesis testing. Whereas we typically control the type I error rate for a single-hypothesis test, a compound error rate is controlled for multiple-hypothesis tests. For example, controlling the ..."
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Cited by 775 (14 self)
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Summary. Multiple-hypothesis testing involves guarding against much more complicated errors than single-hypothesis testing. Whereas we typically control the type I error rate for a single-hypothesis test, a compound error rate is controlled for multiple-hypothesis tests. For example, controlling
Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments
- STATISTICA SINICA
, 2002
"... DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identification of differentially expressed genes in replicated cDNA microarray experiments. A ..."
Abstract
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Cited by 438 (12 self)
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test for each gene of the null hypothesis of no association between the expression levels and responses or covariates of interest. Di erentially expressed genes are identified based on adjusted p-values for a multiple testing procedure which strongly controls the family-wise Type I error rate and takes
Multicast-Based Inference of Network-Internal Characteristics: Accuracy of Packet Loss Estimation
- IEEE Transactions on Information Theory
, 1998
"... We explore the use of end-to-end multicast traffic as measurement probes to infer network-internal characteristics. We have developed in an earlier paper [2] a Maximum Likelihood Estimator for packet loss rates on individual links based on losses observed by multicast receivers. This technique explo ..."
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Cited by 323 (40 self)
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. In particular, we report on the error between inferred loss rates and actual loss rates as we vary the network topology, propagation delay, packet drop policy, background traffic mix, and probe traffic type. In all but one case, estimated losses and probe losses agree to within 2 percent on average. We feel
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
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
multcomp: Simultaneous Inference in General Parametric Models,
, 2008
"... Abstract Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be ..."
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Cited by 234 (6 self)
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to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described
Pixy: A Static Analysis Tool for Detecting Web Application Vulnerabilities (Short Paper)
- IN 2006 IEEE SYMPOSIUM ON SECURITY AND PRIVACY
, 2006
"... The number and the importance of Web applications have increased rapidly over the last years. At the same time, the quantity and impact of security vulnerabilities in such applications have grown as well. Since manual code reviews are time-consuming, error-prone and costly, the need for automated so ..."
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Cited by 212 (23 self)
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applications. The observed false positive rate is at around 50 % (i.e., one false positive for each vulnerability) and therefore, low enough to permit effective security audits.
Deconvolution with unknown error distribution
- IN ANNALS OF STATISTICS
, 2009
"... We consider the problem of estimating a density fX using a sample Y1,...,Yn from fY = fX ⋆ fǫ, where fǫ is an unknown density. We assume that an additional sample ǫ1,...,ǫm from fǫ is observed. Estimators of fX and its derivatives are constructed by using nonparametric estimators of fY and fǫ and by ..."
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Cited by 24 (4 self)
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and by applying a spectral cut-off in the Fourier domain. We derive the rate of convergence of the estimators in case of a known and unknown error density fǫ, where it is assumed that fX satisfies a polynomial, logarithmic or general source condition. It is shown that the proposed estimators are asymptotically
Structural Matching in Computer Vision Using Probabilistic Reasoning
, 1995
"... easurement error distributions is dependent on the type of geometric feature, the measurement noise model and the nature of the unknown scene-to-model transformation: some examples are presented. A number of variations on the basic labelling algorithm are described, of which some have implications f ..."
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Cited by 201 (15 self)
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easurement error distributions is dependent on the type of geometric feature, the measurement noise model and the nature of the unknown scene-to-model transformation: some examples are presented. A number of variations on the basic labelling algorithm are described, of which some have implications
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