
Inference and Prediction in Large Dimensions
Wiley (Publisher)
Will be published approx. on 19. October 2007
Book
Hardback
336 pages
978-0-470-01761-6 (ISBN)
Description
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.
Reviews / Votes
"This book provides a rigorous and thorough account of modern mathematical statistics as applied to the classic problems of prediction, filtering, inference with kernels, and high-dimensional linear processes ... All in all, Large Sample Techniques in Statistics is an excellent book that I recommend whole-heartedly." (Journal of the American Statistical Association, 1 December 2011)More details
Series
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 233 mm
Width: 173 mm
Thickness: 24 mm
Weight
608 gr
ISBN-13
978-0-470-01761-6 (9780470017616)
Schweitzer Classification
Other editions
Additional editions

Denis Bosq | Delphine Balnke
Inference and Prediction in Large Dimensions
E-Book
08/2008
Wiley
€91.99
Available for download
Persons
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.
Content
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.