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A new strategy for Robbins’ problem of optimal stopping

Published online by Cambridge University Press:  04 April 2017

Martin Meier*
Affiliation:
IHS Vienna
Leopold Sögner*
Affiliation:
IHS Vienna
*
* Postal address: Department of Economics and Finance, Institute for Advanced Studies, Josefstädter Straße 39, 1080 Vienna, Austria.
* Postal address: Department of Economics and Finance, Institute for Advanced Studies, Josefstädter Straße 39, 1080 Vienna, Austria.

Abstract

In this paper we study the expected rank problem under full information. Our approach uses the planar Poisson approach from Gnedin (2007) to derive the expected rank of a stopping rule that is one of the simplest nontrivial examples combining rank dependent rules with threshold rules. This rule attains an expected rank lower than the best upper bounds obtained in the literature so far, in particular, we obtain an expected rank of 2.326 14.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 2017 

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References

[1] Assaf, D. and Samuel-Cahn, E. (1996).The secretary problem: minimizing the expected rank with i.i.d. random variables.Adv. Appl. Prob. 28,828852.Google Scholar
[2] Bruss, F. T. (2005).What is known about Robbins’ problem?.J. Appl. Prob. 42,108120.Google Scholar
[3] Bruss, F. T. and Delbaen, F. (2001).Optimal rules for the sequential selection of monotone subsequences of maximum expected length.Stoch. Process. Appl. 96,313342.Google Scholar
[4] Bruss, F. T. and Ferguson, T. S. (1993).Minimizing the expected rank with full information.J. Appl. Prob. 30,616626.Google Scholar
[5] Bruss, F. T. and Ferguson, T. S. (1995).Half-prophets and Robbins’ problem of minimizing the expected rank. In Athens Conference on Applied Probability and Time Series Analysis, Vol. I (Lect. Notes Statist. 114, 1996).Springer,New York, pp.117.Google Scholar
[6] Bruss, F. T. and Swan, Y. C. (2009).A continuous-time approach to Robbins’ problem of minimizing the expected rank.J. Appl. Prob. 46,118.CrossRefGoogle Scholar
[7] Gnedin, A. V. (1996).On the full information best-choice problem.J. Appl. Prob. 33,678687.Google Scholar
[8] Gnedin, A. V. (2004).Best choice from the planar Poisson process.Stoch. Process. Appl. 111,317354.Google Scholar
[9] Gnedin, A. V. (2009).Optimal stopping with rank-dependent loss.J. Appl. Prob. 44,9961011.Google Scholar
[10] Gnedin, A. and Iksanov, A. (2011).Moments of random sums and Robbins’ problem of optimal stopping.J. Appl. Prob. 48,11971199.Google Scholar
[11] Meier, M. and Sögner, L. (2016).A new strategy for Robbins’ problem of optimal stopping. Preprint. Available at http://arxiv.org/abs/1606.03348.Google Scholar
[12] Swan, Y. (2011).A contribution to the study of Robbins’ problem. Doctoral Thesis, Université de Liége.Google Scholar
[13] Tamaki, M. (2004).The PPP approach to Robbins’ problem of minimizing the expected rank.RIMS Kokyuroku 1383,130139.Google Scholar