Institute of Mathematical Statistics Collections
On Detecting Fake Coin Flip Sequences
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.
First available in Project Euclid: 28 January 2009
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Copyright © 2008, Institute of Mathematical Statistics
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.
-  Csörgő, M. and Révész, P. (1981). Strong Approximations in Probability and Statistics. Academic Press.
-  Emrich, L. J. and Piedmonte, M. R. (1991). A method for generating high dimensional multivariate binary variates The American Statistician 45 (4) 302–304.
-  Gange, S. J. (1995). Generating multivariate categorical variates using the iterative proportional fitting algorithm. The American Statistician 49 (2).
-  Hill, T. P. (1999). The difficulty of faking data. Chance Magazine 12 (3) 27–31.
-  Lee, A. J. (1993). Generating random binary deviates having fixed marginal distributions and specified degrees of association. The American Statistician 47 (3).
-  Park, C. G., Park, T. and Shin, D. W. (1996). A simple method for generating correlated binary variates. The American Statistician 50 (4).
-  Prentice, R. L. (1998). Correlated binary regression with covariates specific to each binary observation. Biometrics 44 (4) 1033–1048.