Results 291 to 300 of about 9,151,209 (331)

THE CALCULATION OF CONFIDENCE REGIONS FOR EIGENVECTORS

Australian Journal of Statistics, 1984
summaryAn explicit algorithm is given for constructing a confidence region on the appropriate unit sphere for an eigenvector, given a large sample. It is assumed the eigenvector corresponds to the largest eigenvalue of ExxT, a matrix with distinct eigenvalues, and that the estimation uses n‐1S̀xixixT.
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Fuzzy Confidence Regions

2016
Confidence regions are usually based on exact data. However, continuous data are always more or less non-precise, also called fuzzy. For fuzzy data the concept of confidence regions has to be generalized. This is possible and the resulting confidence regions are fuzzy subsets of the parameter space.
Reinhard Viertl, Shohreh Mirzaei Yeganeh
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Confidence regions of planar cardiac vectors

Journal of Electrocardiology, 1980
A method is presented for plotting the 90%, 95%, and 99% confidence regions of planar cardiac vectors based on the bivariate normal distribution.
S, Dubin, A, Herr, P, Hunt
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Regional Concentration and Confidence Regions

2015
Industries necessarily differ with respect to their type of geographical concentration. When some industries are overrepresented in urban areas (urban concentration), then some other industries must be overrepresented in rural areas (rural concentration).
Stepanyan, Andranik, von Auer, Ludwig, Trede, Mark   +2 more
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Tests and Confidence Regions

1990
Consider the example of house prices that we saw in the last chapter. It is reasonable to ask one or more of the following questions: (a) Is the selling price affected by the number of rooms in a house, given that the other independent variables (e.g., floor area, lot size) remain the same?
Ashish Sen, Muni Srivastava
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Regional confidence bands for ROC curves

Statistics in Medicine, 2000
The performance of a diagnostic test is characterised by its specificity and sensitivity. For a quantitative diagnostic test these criteria depend on the selected cut-off point. The receiver operating characteristic (ROC) curve of a quantitative diagnostic test is generated by plotting sensitivity against specificity as the cut-off point runs through ...
K, Jensen, H H, Müller, H, Schäfer
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Confidence Regions in Models of Ordered Data

Journal of Statistical Theory and Practice, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bedbur, S., Lennartz, J. M., Kamps, U.
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Building confidence regions for the ROC surface

Pattern Recognition Letters, 2014
The ROC surface is the major criterion for assessing the accuracy of diagnosis test statistics s(X) in regard to their capacity of discriminating between K>=3 statistical populations. It provides additionally a widely used visual tool in the cases K=2 and K=3.
Stéphan Clémençon, Sylvain Robbiano
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On confidence regions in canonical variate analysis

Biometrika, 1989
Canonical variate analysis finds for multivariate data in which sample members come from several groups those linear combinations of the original variables, termed the canonical variates, which successively maximize between-groups variances relative to within-group variances.
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Bootstrap confidence regions in multinomial sampling

Applied Mathematics and Computation, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Domingo Morales, Leandro Pardo, Laureano Santamaría   +2 more
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