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VOL. 4 | 2008 On Detecting Fake Coin Flip Sequences
Michael A. Kouritzin, Fraser Newton, Sterling Orsten, Daniel C. Wilson

Editor(s) Stewart N. Ethier, Jin Feng, Richard H. Stockbridge

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.

Information

Published: 1 January 2008
First available in Project Euclid: 28 January 2009

zbMATH: 1175.60036
MathSciNet: MR2574227

Digital Object Identifier: 10.1214/074921708000000336

Rights: Copyright © 2008, Institute of Mathematical Statistics

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