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Estimating Customer Loss Rates from Transactional Data

  • D. J. Daley
  • L. D. Servi
Chapter
  • 644 Downloads
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 19)

Abstract

This chapter considers the problem of making inferences for a transactional dataset in the context of a model that allows for lost customers. We use a Markovian framework (to facilitate computation); subsequently (Daley and Servi [6]), we have relaxed some of the more restrictive of these assumptions.

Keywords

Ergodic Theorem Busy Period Asymptotic Variance Idle Period MIMIc Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Cohen, J. W. The Single Server Queue, revised ed. North-Holland, Amsterdam, 1982.zbMATHGoogle Scholar
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    Daley, D. J. Stochastically monotone Markov chains. Z. Wahrs. 10, 305–317, 1968.MathSciNetzbMATHCrossRefGoogle Scholar
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    Daley, D. J., and Servi, L. D. Exploiting Markov chains to infer queue-length from transactional data. J. Appl. Probab. 29, 713–732, 1992.MathSciNetzbMATHCrossRefGoogle Scholar
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    Daley, D. J., and Servi, L. D. Moment estimation of customer loss rates from transactional data. In preparation.Google Scholar
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    Larson, R. C. The queue inference engine: deducing queue statistics from transactional data. Management Sci. 36, 586–601, Addendum, 36, 1062, 1990.Google Scholar
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    Stoyan, D. Comparison Methods for Queues and Other Stochastic Processes (English ed., edited, with revisions by Daley, D. J.). Wiley, Chichester, 1983.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • D. J. Daley
  • L. D. Servi

There are no affiliations available

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