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A central limit property under a modified Ehrenfest urn design

Published online by Cambridge University Press:  14 July 2016

Yung-Pin Chen*
Affiliation:
Lewis & Clark College
*
Postal address: Department of Mathematical Sciences, Lewis & Clark College, Portland, OR 97219, USA. Email address: ychen@lclark.edu
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Abstract

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We consider a stochastic process in a modified Ehrenfest urn model. The modification prescribes there to be a minimum number of balls in each urn, and the process records the differences between treatment assignments under a sampling scheme implemented with this modified Ehrenfest urn model. In contrast to the result that the difference process forms a Markov chain and converges to a stationary distribution under the Ehrenfest urn model, the corresponding process under this modified Ehrenfest urn design satisfies the central limit property. We prove this asymptotic normality property using a central limit theorem for dependent random variables, renewal theory, and two Kolmogorov-type maximal inequalities.

Type
Research Papers
Copyright
© Applied Probability Trust 2006 

References

Billingsley, P. (1968). Convergence of Probability Measures. John Wiley, New York.Google Scholar
Billingsley, P. (1995). Probability and Measure, 3rd edn. John Wiley, New York.Google Scholar
Billingsley, P. (1999). Convergence of Probability Measures, 2nd edn. John Wiley, New York.Google Scholar
Bingham, N. H. (1991). Fluctuation theory for the Ehrenfest urn. Adv. Appl. Prob. 23, 598611.Google Scholar
Blackwell, D. and Hodges, J. L. Jr. (1957). Design for the control of selection bias. Ann. Math. Statist. 28, 449460.Google Scholar
Chen, Y.-P. (2000). Which design is better? Ehrenfest urn versus biased coin. Adv. App. Prob. 32, 738749.Google Scholar
Diaconis, P. (1988). Group Representations in Probability and Statistics. Institute of Mathematical Statistics, Hayward, CA.Google Scholar
Ehrenfest, P. and Ehrenfest, T. (1907). Über zwei bekannte Einwände gegen das Boltzmannsche H-Theorem. Phys. Z. 8, 311314.Google Scholar
Feller, W. (1968). An Introduction to Probability Theory and Its Applications, Vol. 1, 3rd edn. John Wiley, New York.Google Scholar
Hoel, P. G., Port, S. C. and Stone, C. J. (1972). Introduction to Stochastic Processes. Houghton Mifflin, Boston, MA.Google Scholar
Ibragimov, I. (1962). Some limit theorems for stationary processes. Theor. Prob. Appl. 7, 349382.CrossRefGoogle Scholar
Ibragimov, I. (1963). A central limit theorem for a class of dependent random variables. Theor. Prob. Appl. 8, 8389.Google Scholar
Karlin, S. and McGregor, J. (1959). Random walks. Illinois J. Math. 3, 6681.Google Scholar
Karlin, S. and Taylor, M. H. (1981). A Second Course in Stochastic Processes. Academic Press, New York.Google Scholar
Palacios, J. L. (1993). Fluctuation theory for the Ehrenfest urn via electric networks. Adv. Appl. Prob. 25, 472476.Google Scholar
Palacios, J. L. (1994). Another look at the Ehrenfest urn via electric networks. Adv. Appl. Prob. 26, 820824.Google Scholar
Schach, S. (1971). Weak convergence results for a class of multivariate Markov processes. Ann. Math. Statist. 42, 451465.Google Scholar
Serfling, R. J. (1968). Contributions to central limit theory for dependent variables. Ann. Math. Statist. 39, 11581175.Google Scholar
Van Beek, K. W. H. and Stam, A. J. (1987). A variant of the Ehrenfest model. Adv. App. Prob. 19, 995996.CrossRefGoogle Scholar