Nonparametric Functional Data Analysis Theory and Practice /
Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied th...
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| Main Authors: | , |
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| 企業作者: | |
| 格式: | 電子 電子書 |
| 語言: | English |
| 出版: |
New York, NY :
Springer New York : Imprint: Springer,
2006.
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| 版: | 1st ed. 2006. |
| 叢編: | Springer Series in Statistics,
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| 主題: | |
| 在線閱讀: | https://doi.org/10.1007/0-387-36620-2 |
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書本目錄:
- Statistical Background for Nonparametric Statistics and Functional Data
- to Functional Nonparametric Statistics
- Some Functional Datasets and Associated Statistical Problematics
- What is a Well-Adapted Space for Functional Data?
- Local Weighting of Functional Variables
- Nonparametric Prediction from Functional Data
- Functional Nonparametric Prediction Methodologies
- Some Selected Asymptotics
- Computational Issues
- Nonparametric Classification of Functional Data
- Functional Nonparametric Supervised Classification
- Functional Nonparametric Unsupervised Classification
- Nonparametric Methods for Dependent Functional Data
- Mixing, Nonparametric and Functional Statistics
- Some Selected Asymptotics
- Application to Continuous Time Processes Prediction
- Conclusions
- Small Ball Probabilities and Semi-metrics
- Some Perspectives.