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A Monte-Carlo Study of Asymptotically Robust Tests for Correlation Coefficients

G. T. Duncan and M. W. J. Layard
Biometrika
Vol. 60, No. 3 (Dec., 1973), pp. 551-558
Published by: Oxford University Press on behalf of Biometrika Trust
DOI: 10.2307/2335004
Stable URL: http://www.jstor.org/stable/2335004
Page Count: 8
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A Monte-Carlo Study of Asymptotically Robust Tests for Correlation Coefficients
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Abstract

Monte-Carlo simulation is used to compare the small-sample performance of the usual normal theory procedures for inference about correlation coefficients with that of two asymptotically robust procedures, one of which is based on a grouping of the observations and the other on the jackknife technique. The sampled distributions comprise the normal and five nonnormal distributions. The small-sample results support the conclusion based on asymptotic theory that the normal test is not robust. The jackknife procedure works well for most of the sampled distributions.

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