Hostname: page-component-89b8bd64d-mmrw7 Total loading time: 0 Render date: 2026-05-08T11:25:03.422Z Has data issue: false hasContentIssue false
  • English
  • Français

The Pricing Problem

Published online by Cambridge University Press:  27 July 2009

Jaxk H. Reeves ,
Ashim Mallik  and
William P. McCormick
Jaxk H. Reeves
Affiliation:
Department of StatisticsThe University of Georgia, Athens, Georgia 30602
Ashim Mallik
Affiliation:
Department of StatisticsThe University of Georgia, Athens, Georgia 30602
William P. McCormick
Affiliation:
Department of StatisticsThe University of Georgia, Athens, Georgia 30602
Get access Rights & Permissions [Opens in a new window]

Abstract

A sequential procedure to select optimal prices based on maximum likelihood estimation is considered. Asymptotic properties of the pricing scheme and the concommitant estimation problem are examined. For small sample sizes, simulation results show that the proposed procedure has high efficiency relative to the best procedure when the parameter is known.

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 1 , Issue 3 , July 1987 , pp. 349 - 366
Copyright
Copyright © Cambridge University Press 1987

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1Albert, A. E. (1961). The sequential design of experiments for infinitely many states of nature. Ann. Math. Statist. 32: 774–799.Google Scholar
2Brown, B. M. (1971). Martingale central limit theorems. Ann. Math. Statist. 42: 59–66.Google Scholar
3Chernoff, H. (1959). Sequential design of experiments. Ann. Math. Statist. 30: 755–777.Google Scholar
4Hall, P., & Heyde, C. C. (1980). Martingale limit theory and its applications. New York: Academic Press.Google Scholar
5Kiefer, J., & Wolfowitz, J. (1952). Stochastic estimation of the maximum of a regression function. Ann. Math. Statist. 23: 462–466.Google Scholar
6Lehmann, E. L. (1983). Theory of point estimation. New York: Wiley.Google Scholar
7Rothschild, M. (1974). A two-armed bandit theory on market pricing. J. Economic Theory 9: 185–202.Google Scholar
8Schmalensee, R. (1975). Alternate models of bandit selection. J. Economic Theory 10: 333–342.Google Scholar