Nonparametric plug-in classifier for multiclass classification of S.D.E. paths
2022-12-20Code Available0· sign in to hype
Christophe Denis, Charlotte Dion-Blanc, Eddy Ella Mintsa, Viet-Chi Tran
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Abstract
We study the multiclass classification problem where the features come from the mixture of time-homogeneous diffusions. Specifically, the classes are discriminated by their drift functions while the diffusion coefficient is common to all classes and unknown. In this framework, we build a plug-in classifier which relies on nonparametric estimators of the drift and diffusion functions. We first establish the consistency of our classification procedure under mild assumptions and then provide rates of cnvergence under different set of assumptions. Finally, a numerical study supports our theoretical findings.