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Length-Biased Orderings with Applications

Published online by Cambridge University Press:  27 July 2009

Abdulhamid A. Alzaid
Affiliation:
Department of Statistics College of Science King Saud University Riyadh, 11451, Saudi Arabia

Abstract

A new partial ordering generated by the set of star-shaped functions is introduced. This ordering is equivalent to the stochastic comparison of the length-biased distributions which are frequently appropriate for certain natural sampling plans in biometry, reliability, and survival analysis studies. It is shown that the length-biased ordering fits in the framework of stochastic and variatiility orderings. It enjoys many properties similar to those of the stochastic and variability orderings.

Type
Articles
Copyright
Copyright © Cambridge University Press 1988

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References

Gupta, R.C. & Keating, J.P. (1986). Relations for reliability measures under length-biased sampling. Scandinavian Journal of Statistics 13: 4956.Google Scholar
Marshall, A.W. & Olkin, I. (1979). Inequalities: Theory of majorization and its applications. New York: Academic Press.Google Scholar
Ross, S. (1983). Stochastic processes. New York: John Wiley & Sons, Inc.Google Scholar
Ross, S.M. & Schechner, Z. (1984). Some reliability applications of the variability ordering. Operations Research 32: 679687.CrossRefGoogle Scholar
Stoyan, D. (1983). Comparison methods for queues and other stochastic models. New York: John Wiley & Sons, Inc.Google Scholar
Whitt, W. (1980). Uniform conditional stochastic order. Journal of Applied Probability 17: 112123.CrossRefGoogle Scholar
Whitt, W. (1985). Uniform conditional variability ordering of probability distributions. Journal of Applied Probability 22: 619633.CrossRefGoogle Scholar