Professor Orr and Higher Criticism
نویسندگان
چکیده
منابع مشابه
Higher Criticism for Detecting Sparse Heterogeneous Mixtures
Higher Criticism, or second-level significance testing, is a multiple comparisons concept mentioned in passing by Tukey (1976). It concerns a situation where there are many independent tests of significance and one is interested in rejecting the joint null hypothesis. Tukey suggested to compare the fraction of observed significances at a given α-level to the expected fraction under the joint nu...
متن کاملProperties of Higher Criticism under Strong Dependence
The problem of signal detection using sparse, faint information is closely related to a variety of contemporary statistical problems, including the control of false-discovery rate, and classification using very high-dimensional data. Each problem can be solved by conducting a large number of simultaneous hypothesis tests, the properties of which are readily accessed under the assumption of inde...
متن کاملHigher Criticism Statistic: Theory and Applications in Cosmology and Astronomy
Higher Criticism is a recent statistic proposed by Donoho and Jin [1] where it has been shown to be effective at resolving a very subtle testing problem: test whether n normal means are all zero versus the alternative that a small fraction is non-zero. Higher Criticism is also useful for non-Gaussian detection. Motivated by the recent problem of detecting cosmic strings, we consider a setting i...
متن کاملHigher Criticism Statistic: Theory and Applications in Non-gaussian Detection
Higher Criticism is a statistic recently proposed by Donoho and Jin5. It has been shown to be effective in resolving a very subtle testing problem: whether n normal means are all zero versus a small fraction is nonzero. Higher Criticism is also useful for non-Gaussian detection in Cosmic Microwave Background (CMB) data. In this report, we review the theory developed in Donoho and Jin5 and discu...
متن کاملFeature Selection by Higher Criticism Thresholding: Optimal Phase Diagram
We consider two-class linear classification in a high-dimensional, low-sample size setting. Only a small fraction of the features are useful, the useful features are unknown to us, and each useful feature contributes weakly to the classification decision – this setting was called the rare/weak model (RW Model) in [11]. We select features by thresholding feature z-scores. The threshold is set by...
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ژورنال
عنوان ژورنال: The American Journal of Theology
سال: 1908
ISSN: 1550-3283
DOI: 10.1086/478748