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On Detecting Fake Coin Flip Sequences

Michael A. Kouritzin, Fraser Newton, Sterling Orsten, and Daniel C. Wilson

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Abstract

Classification of data as true or fabricated has applications in fraud detection and verification of data samples. In this paper, we apply nonlinear filtering to a simplified fraud-detection problem: classifying coin flip sequences as either real or faked. On the way, we propose a method for generating Bernoulli variables with given marginal probabilities and pair-wise covariances. Finally, we present the empirical performance of the classification algorithm.

Chapter information

Source
Stewart N. Ethier, Jin Feng and Richard H. Stockbridge, eds., Markov Processes and Related Topics: A Festschrift for Thomas G. Kurtz (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008), 107-122

Dates
First available in Project Euclid: 28 January 2009

Permanent link to this document
http://projecteuclid.org/euclid.imsc/1233152938

Digital Object Identifier
doi:10.1214/074921708000000336

Rights
Copyright © 2008, Institute of Mathematical Statistics

Citation

Kouritzin, Michael A.; Newton, Fraser; Orsten, Sterling; Wilson, Daniel C. On Detecting Fake Coin Flip Sequences. Markov Processes and Related Topics: A Festschrift for Thomas G. Kurtz, 107--122, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008. doi:10.1214/074921708000000336. http://projecteuclid.org/euclid.imsc/1233152938.


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References

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