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.
O autorze
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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