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NeymanPearson Testing under Interval Probability by Globally Least Favorable Pairs  A Survey of HuberStrassen Theory and Some Results on its Extension to General Interval Probability
 Journal of Statistical Planning and Inference
, 2002
"... The paper studies the extension of one of the basic issues of classical statistics to interval probability. It is concerned with the Generalized NeymanPearson problem, i.e. an alternative testing problem where both hypotheses are described by interval probability. First the HuberStrassen theor ..."
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Cited by 9 (2 self)
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The paper studies the extension of one of the basic issues of classical statistics to interval probability. It is concerned with the Generalized NeymanPearson problem, i.e. an alternative testing problem where both hypotheses are described by interval probability. First the Huber
Distributed Signal Detection under the NeymanPearson Criterion
 IEEE Transactions on Information Theory
, 2001
"... A procedure for finding the NeymanPearson optimum distributed sensor detectors for cases with statistically dependent observations is described. This is the first valid procedure we have seen for this case. This procedure is based on a Theorem proven in this paper. These results clarify and correct ..."
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Cited by 13 (0 self)
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A procedure for finding the NeymanPearson optimum distributed sensor detectors for cases with statistically dependent observations is described. This is the first valid procedure we have seen for this case. This procedure is based on a Theorem proven in this paper. These results clarify
On the NeymanPearson problem for lawinvariant risk measures and robust utility functionals
 ANNALS OF APPLIED PROBABILITY
, 2004
"... Motivated by optimal investment problems in mathematical finance, we consider a variational problem of Neyman–Pearson type for lawinvariant robust utility functionals and convex risk measures. Explicit solutions are found for quantilebased coherent risk measures and related utility functionals. Ty ..."
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Cited by 26 (4 self)
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Motivated by optimal investment problems in mathematical finance, we consider a variational problem of Neyman–Pearson type for lawinvariant robust utility functionals and convex risk measures. Explicit solutions are found for quantilebased coherent risk measures and related utility functionals
Tuning Support Vector Machines for Minimax and NeymanPearson Classification
, 2009
"... This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and NeymanPearson criteria. In principle, these criteria can be optimized in a straightforward way using a costsensitive SVM. In practice, however, because these criteria require especially accu ..."
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Cited by 7 (0 self)
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This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and NeymanPearson criteria. In principle, these criteria can be optimized in a straightforward way using a costsensitive SVM. In practice, however, because these criteria require especially
The use of the area under the ROC curve in the evaluation of machine learning algorithms
 Pattern Recognition
, 1997
"... AbstractIn this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multilayer Percept ..."
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Cited by 664 (3 self)
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accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used
High confidence visual recognition of persons by a test of statistical independence
 IEEE Trans. on Pattern Analysis and Machine Intelligence
, 1993
"... Abstruct A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a samp ..."
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Cited by 596 (8 self)
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Abstruct A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a
Using confidence intervals in withinsubject designs
 PSYCHONOMIC BULLETIN & REVIEW
, 1994
"... ..."
HighRate Vector Quantization for the NeymanPearson Detection of Correlated Processes
"... This paper investigates the effect of quantization on the performance of the NeymanPearson test. It is assumed that a sensing unit observes samples of a correlated stationary ergodic multivariate process. Each sample is passed through an Npoint quantizer and transmitted to a decision device which ..."
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performs a binary hypothesis test. For any false alarm level, it is shown that the miss probability of the NeymanPearson test converges to zero exponentially as the number of samples tends to infinity, assuming that the observed process satisfies certain mixing conditions. The main contribution
Results 1  10
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2,595