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Activity Number:
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543
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Type:
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Contributed
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Date/Time:
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Thursday, August 10, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305422 |
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Title:
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Inferences on Correlation Coefficients: One-Sample, Independent, and Correlated Cases
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Author(s):
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Kalimuthu Krishnamoorthy and Yanping Xia*+
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Companies:
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University of Louisiana at Lafayette and University of Louisiana at Lafayette
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Address:
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211 Liberty Ave., Apt. 1131, Lafayette, LA, 70508,
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Keywords:
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coverage probabilities ; Fisher's z transformation ; sizes ; UMP test ; uniformly most accurate confidence interval
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Abstract:
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This article concerns inference on the correlation coefficients of a multivariate normal distribution. Inferential procedures based on the concepts of generalized variables and generalized p-values are proposed for elements of a correlation matrix. For simple correlation coefficient, the merits of the generalized confidence limits and other approximate methods are evaluated using a numerical study. The study indicates the proposed generalized confidence limits are uniformly most accurate, even for samples as small as three. The results are extended for comparing two independent correlations, correlated correlations, and nonoverlapping correlated correlations. For each problem, the properties of the generalized variable approach and other asymptotic methods are evaluated using Monte Carlo simulation. The generalized variable approach produces satisfactory results.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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