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Nonparametric Statistics for Stochastic Processes: Estimation and Prediction (Lecture Notes in Statistics) - Softcover
Synopsis
This book provides a mathematically rigorous treatment of the theory of nonparametric estimation and prediction for stochastic processes. It discusses discrete time and continuous time, and the emphasis is on the kernel methods. Several new results are presented concerning optimal and superoptimal convergence rates. How to implement the method is discussed in detail and several numerical results are presented. This book will be of interest to specialists in mathematical statistics and to those who wish to apply these methods to practical problems involving time series analysis.
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About the Author
Denis Bosq is a Professor at the Laboratory of Theoretical and Applied Statistics, University of Pierre & Marie Curie - Paris 6. He has over 100 published papers, 5 books, and is chief editor of the journal 'Statistical Inference for Stochastic Processes' as well as associate editor for the 'Journal of Non-Parametric Statistics'. He is a well-known specialist in the field of non-parametric statistical inference.
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Nonparametric statistics for stochastic processes estimation and prediction
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Nonparametric Statistics for Stochastic Processes : Estimation and Prediction
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Nonparametric Statistics for Stochastic Processes: Estimation and Prediction (Lecture Notes in Statistics)
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Softcover. Condition: Bon. Ancien livre de bibliothèque avec équipements. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Edition 1996. Ammareal gives back up to 15% of this item's net price to charity organizations. Seller Inventory # G-581-266
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