Denis Bosq, Delphine Balnke

Inference and Prediction in Large Dimensions

• Gebundenes Buch

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. Inference and Prediction in Large Dimensions 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.

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In many instances of statistical research the data and/or parameters belong to a large, or infinite, dimensional space. In such circumstances accurate inference and statistical prediction are often problematic, requiring an alternative statistical treatment.

Inference and Prediction in Large Dimensions offers a predominantly theoretical coverage of statistical prediction when such dimensional spaces are involved, and discusses numerous potential applications. The authors develop the theory of statistical prediction, non-parametric estimation by adaptive projection and kernel, with applications to tests of fit and prediction, and theory of linear processes in function spaces with applications to prediction of continuous time processes.

Highlighting the latest developments in the field, this book provides a comprehensive and authoritative introduction to the topic. The text is divided into three main parts covering statistical prediction, inference by projection, and inference by kernels. The applications are demonstrated with examples from fields such as finance, medicine and psychology.

Inference and Prediction in Large Dimensions is aimed at graduates and researchers in the field of statistics, and students specializing in statistical inference for stochastic processes. The many potential applications also make it ideal for applied statisticians in numerous areas, as well as mathematicians and engineers.

Produktdetails

• Wiley Series in Probability and Statistics
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 336
• Erscheinungstermin: 1. Dezember 2007
• Englisch
• Abmessung: 233mm x 173mm x 24mm
• Gewicht: 610g
• ISBN-13: 9780470017616
• ISBN-10: 0470017619
• Artikelnr.: 22825379

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.