This book is about copies-based nonparametric estimation of the drift function in stochastic differential equations (SDEs) driven by Brownian motion, a jump process, or fractional Brownian motion.
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While the estimators of the drift function in SDEs are classically computed from one long-time observation of the ergodic stationary solution, here the estimation framework – which is part of functional data analysis – involves multiple copies of the (non-stationary) solution observed over a short-time interval. Two kinds of nonparametric estimators are investigated for SDE models, first presented in the regression framework: the projection least squares estimator and the Nadaraya-Watson estimator. Adaptive procedures are provided for possible applications in statistical learning [...]. (Estratto dal sito dell'editore)
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*From nonparametric regression to statistical inference for non-ergodic diffusion processes / Nicolas Marie. - Cham : Springer, 2025. - 1 testo elettronico (xii, 184 p. : ill.)
