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Relevant bibliographies by topics / Functional data analysis / Books

Books on the topic 'Functional data analysis'

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Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

1952-, Silverman B. W., ed. Functional data analysis. New York: Springer, 1997.

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2

Ramsay, J. O., and B. W. Silverman. Functional Data Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-7107-7.

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3

Ramsay, J. O., and B. W. Silverman. Functional Data Analysis. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/b98888.

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4

1952-, Silverman B. W., and Knovel (Firm), eds. Functional data analysis. 2nd ed. New York: Springer, 2006.

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5

author, Silverman B. W., ed. Functional Data Analysis. New York, NY: Springer New York, 1997.

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6

The Oxford handbook of functional data analysis. Oxford: Oxford University Press, USA, 2011.

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7

Srivastava, Anuj, and Eric P. Klassen. Functional and Shape Data Analysis. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-4020-2.

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8

Morettin, Pedro A., Aluísio Pinheiro, and Brani Vidakovic. Wavelets in Functional Data Analysis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59623-5.

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9

Analysis of variance for functional data. Boca Raon: CRC Press, Taylor & Francis Group, 2014.

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10

Poldrack, Russell A. Handbook of functional MRI data analysis. Cambridge: Cambridge University Press, 2011.

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11

Taeryon, Choi, ed. Gaussian process regression analysis for functional data. Boca Raton, FL: CRC Press, 2011.

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12

Horváth, Lajos. Inference for functional data with applications. New York, N.Y: Springer Verlag, 2012.

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13

Philippe, Vieu, ed. Nonparametric functional data analysis: Theory and practice. New York: Springer, 2006.

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14

Saha, P. Principles of data analysis. Great Malvern: Cappella Archive, 2004.

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15

Ferraty, Frédéric. Nonparametric functional data analysis: Theory and practice. New York: Springer, 2006.

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16

Ramsay, James, Giles Hooker, and Spencer Graves. Functional Data Analysis with R and MATLAB. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98185-7.

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17

Amoozegar, Cyrus Bobak. Spatiotemporal Analysis of Functional Dynamic Imaging Data. [New York, N.Y.?]: [publisher not identified], 2014.

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18

Spencer, Graves, Hooker Giles, and SpringerLink (Online service), eds. Functional Data Analysis with R and MATLAB. New York, NY: Springer-Verlag New York, 2009.

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19

Ferraty, Frederic. Nonparametric functional data analysis: Theory and practice. New York, NY: Springer, 2006.

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20

The statistical analysis of functional MRI data. New York: Springer, 2008.

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21

Kantz, H. Nonlinear Analysis of Physiological Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998.

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22

Ramsay, James O., and Bernard W. Silverman, eds. Applied Functional Data Analysis: Methods and Case Studies. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/b98886.

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23

B, Clarkson Douglas, ed. S+ functional data analysis: User's manual for Windows. New York: Springer Science+Business Media, 2005.

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24

Chen, Siying. Integration of Functional Genomic Data in Genetic Analysis. [New York, N.Y.?]: [publisher not identified], 2021.

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25

T, Sommer Friedrich, and Wichert Andrzej, eds. Exploratory analysis and data modeling in functional neuroimaging. Cambridge, Mass: MIT Press, 2003.

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26

Angel Garcia de la Garza. Functional Data Analysis and Machine Learning for High-Dimensional Structured Data. [New York, N.Y.?]: [publisher not identified], 2022.

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27

Ferraty, Frédéric, ed. Recent Advances in Functional Data Analysis and Related Topics. Heidelberg: Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2736-1.

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28

Recent advances in functional data analysis and related topics. Berlin: Physica-Verlag, 2011.

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29

Kohno, Mitsuhiko. Global Analysis in Linear Differential Equations. Dordrecht: Springer Netherlands, 1999.

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30

Aas, J. Data exchange in finite element analysis: A functional requirement specification. East Kilbride, Glasgow: NAFEMS, 1992.

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31

Hsing, Tailen, and Randall Eubank. Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118762547.

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32

inc, American Management Systems. Functional requirements analysis for the Bureau of Land Management ADP modernization project. [Arlington, Va.]: American Management Systems, 1986.

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33

Functional Data Analysis. Springer, 2010.

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34

James, Ramsay, and B. W. Silverman. Functional Data Analysis. Springer London, Limited, 2006.

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35

Applied Functional Data Analysis. Springer, 2002.

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36

Mahiques, Jorge Mateu, and Ramon Giraldo. Geostatistical Functional Data Analysis. Wiley & Sons, Incorporated, John, 2021.

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37

Mahiques, Jorge Mateu, and Ramon Giraldo. Geostatistical Functional Data Analysis. Wiley & Sons, Incorporated, John, 2021.

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38

Mahiques, Jorge Mateu, and Ramon Giraldo. Geostatistical Functional Data Analysis. Wiley & Sons, Incorporated, John, 2021.

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39

Mateu, Jorge, and Ramón Giraldo, eds. Geostatistical Functional Data Analysis. Wiley, 2021. http://dx.doi.org/10.1002/9781119387916.

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40

Nonparametric Functional Data Analysis. Springer New York, 2006. http://dx.doi.org/10.1007/0-387-36620-2.

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41

S+ Functional Data Analysis. New York: Springer-Verlag, 2005. http://dx.doi.org/10.1007/0-387-28393-5.

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42

Mahiques, Jorge Mateu, and Ramon Giraldo. Geostatistical Functional Data Analysis. Wiley & Sons, Limited, John, 2021.

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43

Srivastava, Anuj, and Eric P. Klassen. Functional and Shape Data Analysis. Springer, 2016.

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44

Reimherr, Matthew, and Piotr Kokoszka. Introduction to Functional Data Analysis. Taylor & Francis Group, 2017.

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45

James, Gareth. Sparseness and functional data analysis. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.11.

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Abstract:

This article considers two functional data analysis settings where sparsity becomes important: the first involves only measurements at a relatively sparse set of points and the second relates to variable selection in a functional case. It begins with a discussion of two data sets that fall into the ‘sparsely observed’ category, the ‘growth’ data and the ‘nephropathy’ data, both of which are used to illustrate alternative approaches for analysing sparse functional data. It then examines different classes of methods that can be applied to functional data, such as basis functions, mixed-effects models and local smoothing techniques, as well as specific methodologies for dealing with sparse functional data in the principal components, clustering, classification, and regression settings. Finally, it describes two approaches for performing regressions involving a functional predictor and a scalar response: SASDA (sequential algorithm for selecting design automatically) and FLiRTI (Functional Linear Regression That’s Interpretable).

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46

Kokoszka, Piotr, and Matthew Reimherr. Introduction to Functional Data Analysis. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315117416.

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47

Introduction to Functional Data Analysis. Taylor & Francis Group, 2017.

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48

Reimherr, Matthew, and Piotr Kokoszka. Introduction to Functional Data Analysis. Taylor & Francis Group, 2017.

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49

Vidakovic, Brani, Pedro A. Morettin, and Aluísio Pinheiro. Wavelets in Functional Data Analysis. Springer, 2017.

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50

Reimherr, Matthew, and Piotr Kokoszka. Introduction to Functional Data Analysis. Taylor & Francis Group, 2017.

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