Denis Bosq, Delphine Balnke

Inference and Prediction in Large Dimensions (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 336
• Erscheinungstermin: 2. August 2008
• Englisch
• ISBN-13: 9780470724026
• Artikelnr.: 37298528

Autorenporträt

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.

Inhaltsangabe

List of abbreviations. Introduction. Part I: Statistical Prediction Theory.
1. Statistical Prediction. 2. Asymptotic Prediction. Part II: Inference by
Projection. 3. Estimation by adaptive projection. 4. Functional tests of
fit. 5. Prediction by projection. Part III: Inference by Kernels. 6. Kernel
method in discrete time. 7. Kernel method in continuous time. 8. Kernel
method from sampled data. Part IV: Local Time. 9. The empirical density.
Part V: Linear Processes in High Dimensions. 10. Functional linear
processes. 11. Estimation and prediction of functional linear processes.
Appendix. bibliography. Index.