2004
DOI: 10.1007/978-0-387-21736-9
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All of Statistics

Abstract: All of statistics: a concise course in statistica! inference 1 Larry a. W asserman.p. cm. -(Springer texts in statistics) Includes bibliographical references and index.

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Cited by 1,036 publications
(488 citation statements)
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“…Standard classification methods operate on feature vectors that numerically represent the properties of the data that are judged relevant for the discrimination task (Hastie et al 2009;Wasserman 2004). There is some freedom in how the feature vectors are constructed.…”
Section: Measuring Discrepancy Via Classificationmentioning
confidence: 99%
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“…Standard classification methods operate on feature vectors that numerically represent the properties of the data that are judged relevant for the discrimination task (Hastie et al 2009;Wasserman 2004). There is some freedom in how the feature vectors are constructed.…”
Section: Measuring Discrepancy Via Classificationmentioning
confidence: 99%
“…As θ approaches θ • , the discriminability (best classification accuracy) of X and Y θ drops. We propose to use the discriminability as discrepancy measure for likelihood-free inference classification rule h * θ , which consists in assigning a feature vector to X if it is more probable that the feature belongs to X than to Y θ , and vice versa for Y θ (Hastie et al 2009;Wasserman 2004). We denote this largest classification accuracy by J * n (θ ),…”
Section: Measuring Discrepancy Via Classificationmentioning
confidence: 99%
See 1 more Smart Citation
“…Covariance is a measure of how much two random variables vary together (Wasserman, 2004). If two variables tend to vary together (that is, when one variable is above its expected value and the other one tends to be also above its expected value), then the covariance between them will be positive.…”
Section: Music Collectionmentioning
confidence: 99%
“…We apply the method of temporal scan using the popular Chi-Square test and Fisher's exact tests of significance [7]. In temporal scan, a time window of interest, d-days wide, is moved along the time axis and at each discrete step (each day in our case), the numbers of detects and non-detects are aggregated across the d days inside the window and, separately, across all of the remaining days outside of the window.…”
Section: Temporal Scanmentioning
confidence: 99%