Cox process functional learning

Abstract : This article addresses the problem of supervised classification of Cox process trajectories, whose random intensity is driven by some exogenous random covariable. The classification task is achieved through a regularized convex empirical risk minimization procedure, and a nonasymptotic oracle inequality is derived. We show that the algorithm provides a Bayes-risk consistent classifier. Furthermore, it is proved that the classifier converges at a rate which adapts to the unknown regularity of the intensity process. Our results are obtained by taking advantage of martingale and stochastic calculus arguments, which are natural in this context and fully exploit the functional nature of the problem.
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Statistical Inference for Stochastic Processes, Springer Verlag, 2015, 18 (3), pp.257-277. <10.1007/s11203-015-9115-z>
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https://hal.archives-ouvertes.fr/hal-00820838
Contributeur : Quentin Paris <>
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Dernière modification le : jeudi 27 avril 2017 - 09:46:34
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Gérard Biau, Benoît Cadre, Quentin Paris. Cox process functional learning. Statistical Inference for Stochastic Processes, Springer Verlag, 2015, 18 (3), pp.257-277. <10.1007/s11203-015-9115-z>. <hal-00820838>

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