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Statistical Inference for Pr(Y < X): The Normal Case
Benjamin Reiser and Irwin Guttman
Vol. 28, No. 3 (Aug., 1986), pp. 253-257
Published by: Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality
Stable URL: http://www.jstor.org/stable/1269081
Page Count: 5
You can always find the topics here!Topics: Approximation, Confidence limits, Statistical estimation, Simulations, Statistics, Statistical inferences, Statistical discrepancies, Mechanism design, Churches, Estimation methods
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This article examines statistical inference for Pr(Y < X), where X and Y are independent normal variates with unknown means and variances. The case of unequal variances is stressed. X can be interpreted as the strength of a component subjected to a stress Y, and Pr(Y < X) is the component's reliability. Two approximate methods for obtaining confidence intervals and an approximate Bayesian probability interval are obtained. The actual coverage probabilities of these intervals are examined by simulation.
Technometrics © 1986 American Statistical Association