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...
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Edizione: | 1st ed. 2006. |
| Serie: | Springer Series in Statistics
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| Soggetti: | |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Description d'après consultation du 14 avril 2011 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Nonparametric functional data analysis, theory and practice, Frédéric Ferraty, Philippe Vieu, 2006, New York, Springer, 1 volume (XX-258 pages), Springer series in statistics, 978-0387-30369-7 |
Sommario:
- 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