Inference and Prediction in Large Dimensions
Mar 2008 · John Wiley & Sons
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This book offers a predominantly theoretical coverage of statistical prediction, with some potential applications discussed, when data and/ or parameters belong to a large or infinite dimensional space. It develops the theory of statistical prediction, non-parametric estimation by adaptive projection – with applications to tests of fit and prediction, and theory of linear processes in function spaces with applications to prediction of continuous time processes.
This work is in the Wiley-Dunod Series co-published between Dunod (www.dunod.com) and John Wiley and Sons, Ltd.
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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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