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Wednesday, May 20, 2020 | History

4 edition of Nonparametric functional data analysis found in the catalog.

Nonparametric functional data analysis

Frederic Ferraty

Nonparametric functional data analysis

theory and practice

by Frederic Ferraty

  • 103 Want to read
  • 27 Currently reading

Published by Springer in New York, NY .
Written in English


Edition Notes

StatementFrédéric Ferraty, Philippe Vieu.
Classifications
LC ClassificationsQA
The Physical Object
Paginationxx, 258 p. ;
Number of Pages258
ID Numbers
Open LibraryOL22733056M
ISBN 100387303693

Use of differentiated log data, separated in years: Issue in this case in low number of samples compared to the number of variables. Now for the real fun Adapted spaces for functional data, and local .   Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that .

  Nonparametric statistics refer to a statistical method in which the data is not required to fit a normal distribution. Nonparametric statistics uses data that is often ordinal, meaning it does not. (). Nonparametric Functional Data Analysis: Theory and Practice. Journal of the American Statistical Association: Vol. , No. , pp. Cited by: 1.

Nonparametric Functional Data Analysis Autor Frédéric Ferraty, Philippe Vieu. Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, . F Chapter Introduction to Nonparametric Analysis Testing for Normality Many parametric tests assume an underlying normal distribution for the population. If your data do not meet this assumption, .


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Nonparametric functional data analysis by Frederic Ferraty Download PDF EPUB FB2

Several functional datasets in chemometrics, econometrics, and pattern recognition are used to emphasize the wide scope of nonparametric functional data analysis in applied sciences.

The Brand: Frédéric Ferraty. Several functional datasets in chemometrics, econometrics, and pattern recognition are used to emphasize the wide scope of nonparametric functional data analysis in applied sciences.

The. Timmermans C, Delsol L and Von Sachs R () Using Bagidis in nonparametric functional data analysis, Journal of Multivariate Analysis,(), Online publication date: 1-Mar. Nonparametric Functional Data Analysis Theory and Practice Ap Springer functional elds. This book presents in a original way new nonparametric statistical meth- Part II Nonparametric File Size: 96KB.

In addition, a functional data is an observation of a functional variable. This book proposes new methodologies for studying functional data in a nonparametric way. Various methods dealing with. The Tecator data is a benchmark data set in the functional data analysis (FDA), and many FDA methods are applied to analyze this data set (Lian and Li, ; Ferraty and Vieu, ).

We apply our. Recent Advances and Trends in Nonparametric Statistics. Book • Edited by: Select Functional data analysis in evolutionary biology. and it is of importance, for instance, in an initial analysis within.

“This book is short and offers quick reference on common techniques for application of wavelets on functional data analysis using some real data examples. The authors have provided code examples in. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas.

At the same time it shows how functional data can be studied. Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation. This book attempts to be exhaustive in nature and is written both. Note: If you're looking for a free download links of Nonparametric Functional Data Analysis (Springer Series in Statistics) Pdf, epub, docx and torrent then this site is not for you.

only do. Functional data analysis (FDA) is a branch of statistics that analyzes data providing information about curves, surfaces or anything else varying over a continuum. In its most general form, under an FDA. Preface. Non-parametric regression methods for longitudinal data analysis have been a popular statistical research topic since the late s.

The needs of longitudinal data analysis from biomedical research. Almost as soon as we had completed our previous book Functional Data Analysis init became clear that potential interest in the?eld was far wider than the audience for the 4/5(1).

Functional nonparametric prediction methodologies.- Some selected asymptotics.- Computational issues.- Nonparametric supervised classification for functional data.- Nonparametric unsupervised. Get this from a library. Nonparametric functional data analysis: theory and practice.

[Frédéric Ferraty; Philippe Vieu] -- Modern apparatuses allow us to collect samples of functional data, mainly curves but. The emphasis in this book is on the application of nonparametric statistical methods.

Wherever Wherever available, the examples and exercises use rea l data, gleaned primary from the results of. Nonparametric Functional Estimation is a compendium of papers, written by experts, in the area of nonparametric functional estimation.

This book attempts to be exhaustive in nature and is Book Edition: 1. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and. General functional data analysis fda provides functions to enable all aspects of functional data analysis: It includes object-types for functional data with corresponding functions for smoothing, plotting and Cited by: 1.

The first meaning of nonparametric covers techniques that do not rely on data belonging to any particular parametric family of probability distributions. These include, among others: distribution free methods.

Functional Data Analysis Some More References Other monographs: Kokoszka & Reimherr,Introduction to Functional Data Analysis Horvath & Kokoszka,Inference for Functional Data .Both methods involve the nonparametric estimation on function θ() and the parametric estimation on parameter β.

For independent data, Rice () pointed out that at the opti-mal bandwidth for .