Nonparametric statistics for stochastic processes : estimation and prediction /
This book provides a mathematically rigorous treatment of the theory of nonparametric estimation and prediction for stochastic processes. It discusses discrete time and continuous time, and the emphasis is on the kernel methods. Several new results are presented concerning optimal and superoptimal c...
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| Format: | eBook |
| Language: | English |
| Published: |
New York :
Springer,
[1996]
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| Series: | Lecture notes in statistics (Springer-Verlag) ;
v. 110. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | This book provides a mathematically rigorous treatment of the theory of nonparametric estimation and prediction for stochastic processes. It discusses discrete time and continuous time, and the emphasis is on the kernel methods. Several new results are presented concerning optimal and superoptimal convergence rates. How to implement the method is discussed in detail and several numerical results are presented. This book will be of interest to specialists in mathematical statistics and to those who wish to apply these methods to practical problems involving time series analysis. |
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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (xii, 169 pages) : illustrations. |
| Format: | Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |
| Bibliography: | Includes bibliographical references (pages 156-165) and index. |
| ISBN: | 9781468404890 (electronic bk.) 146840489X (electronic bk.) |