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Structural components in functional data.

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Structural components in functional data. / Park, Juhyun; Gasser, Theo; Rousson, Valentin.

In: Computational Statistics and Data Analysis, Vol. 53, No. 9, 01.07.2009, p. 3452-3465.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Park, J, Gasser, T & Rousson, V 2009, 'Structural components in functional data.', Computational Statistics and Data Analysis, vol. 53, no. 9, pp. 3452-3465. https://doi.org/10.1016/j.csda.2009.02.024

APA

Park, J., Gasser, T., & Rousson, V. (2009). Structural components in functional data. Computational Statistics and Data Analysis, 53(9), 3452-3465. https://doi.org/10.1016/j.csda.2009.02.024

Vancouver

Park J, Gasser T, Rousson V. Structural components in functional data. Computational Statistics and Data Analysis. 2009 Jul 1;53(9):3452-3465. https://doi.org/10.1016/j.csda.2009.02.024

Author

Park, Juhyun ; Gasser, Theo ; Rousson, Valentin. / Structural components in functional data. In: Computational Statistics and Data Analysis. 2009 ; Vol. 53, No. 9. pp. 3452-3465.

Bibtex

@article{19c655bf07f44f2e9d10788291773dc1,
title = "Structural components in functional data.",
abstract = "Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space. The representation in terms of eigenfunctions is optimal in the sense of L2 approximation. However, the eigenfunctions are not always directed towards an interesting and interpretable direction in the context of functional data and thus could obscure the underlying structure. To overcome such difficulty, an alternative to functional principal component analysis is proposed that produces directed components which may be more informative and easier to interpret. These structural components are similar to principal components, but are adapted to situations in which the domain of the function may be decomposed into disjoint intervals such that there is effectively independence between intervals and positive correlation within intervals. The approach is demonstrated with synthetic examples as well as real data. Properties for special cases are also studied.",
author = "Juhyun Park and Theo Gasser and Valentin Rousson",
note = "The final, definitive version of this article has been published in the Journal, Computational Statistics & Data Analysis 53 (9), 2009, {\textcopyright} ELSEVIER.",
year = "2009",
month = jul,
day = "1",
doi = "10.1016/j.csda.2009.02.024",
language = "English",
volume = "53",
pages = "3452--3465",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",
number = "9",

}

RIS

TY - JOUR

T1 - Structural components in functional data.

AU - Park, Juhyun

AU - Gasser, Theo

AU - Rousson, Valentin

N1 - The final, definitive version of this article has been published in the Journal, Computational Statistics & Data Analysis 53 (9), 2009, © ELSEVIER.

PY - 2009/7/1

Y1 - 2009/7/1

N2 - Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space. The representation in terms of eigenfunctions is optimal in the sense of L2 approximation. However, the eigenfunctions are not always directed towards an interesting and interpretable direction in the context of functional data and thus could obscure the underlying structure. To overcome such difficulty, an alternative to functional principal component analysis is proposed that produces directed components which may be more informative and easier to interpret. These structural components are similar to principal components, but are adapted to situations in which the domain of the function may be decomposed into disjoint intervals such that there is effectively independence between intervals and positive correlation within intervals. The approach is demonstrated with synthetic examples as well as real data. Properties for special cases are also studied.

AB - Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space. The representation in terms of eigenfunctions is optimal in the sense of L2 approximation. However, the eigenfunctions are not always directed towards an interesting and interpretable direction in the context of functional data and thus could obscure the underlying structure. To overcome such difficulty, an alternative to functional principal component analysis is proposed that produces directed components which may be more informative and easier to interpret. These structural components are similar to principal components, but are adapted to situations in which the domain of the function may be decomposed into disjoint intervals such that there is effectively independence between intervals and positive correlation within intervals. The approach is demonstrated with synthetic examples as well as real data. Properties for special cases are also studied.

U2 - 10.1016/j.csda.2009.02.024

DO - 10.1016/j.csda.2009.02.024

M3 - Journal article

VL - 53

SP - 3452

EP - 3465

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

IS - 9

ER -