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On the effect of curve alignment and functional PCA

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On the effect of curve alignment and functional PCA. / Park, Juhyun.

Functional and operatorial statistics. ed. / Sophie Dabo-Niang; Frédéric Ferraty. Springer, 2008. p. 243-245.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Park, J 2008, On the effect of curve alignment and functional PCA. in S Dabo-Niang & F Ferraty (eds), Functional and operatorial statistics. Springer, pp. 243-245. https://doi.org/10.1007/978-3-7908-2062-1_37

APA

Park, J. (2008). On the effect of curve alignment and functional PCA. In S. Dabo-Niang, & F. Ferraty (Eds.), Functional and operatorial statistics (pp. 243-245). Springer. https://doi.org/10.1007/978-3-7908-2062-1_37

Vancouver

Park J. On the effect of curve alignment and functional PCA. In Dabo-Niang S, Ferraty F, editors, Functional and operatorial statistics. Springer. 2008. p. 243-245 https://doi.org/10.1007/978-3-7908-2062-1_37

Author

Park, Juhyun. / On the effect of curve alignment and functional PCA. Functional and operatorial statistics. editor / Sophie Dabo-Niang ; Frédéric Ferraty. Springer, 2008. pp. 243-245

Bibtex

@inproceedings{6593347b358348a8bae4e5a3925e319f,
title = "On the effect of curve alignment and functional PCA",
abstract = "When dealing with multiple curves as functional data, it is a common practice to apply functional PCA to summarise and characterise random variation infinite dimension. Often functional data however exhibits additional time variability that distorts the assumed common structure. This is recognized as the problem of curve registration. While the registration step is routinely employed, this is considered as a preprocessing step prior to any serious analysis. Consequently, the effect of alignment is mostly ignored in subsequent analyses and is not well understood. We revisit the issue by particularly focusing on the effect of time variability on the FPCA and illustrate the phenomena from a borrowed perturbation viewpoint. The analysis further suggests an iterative estimating procedure to optimise FPCA.",
author = "Juhyun Park",
note = "The original publication is available at www.link.springer.com",
year = "2008",
doi = "10.1007/978-3-7908-2062-1_37",
language = "English",
isbn = "9783790820614",
pages = "243--245",
editor = "Sophie Dabo-Niang and Fr{\'e}d{\'e}ric Ferraty",
booktitle = "Functional and operatorial statistics",
publisher = "Springer",

}

RIS

TY - GEN

T1 - On the effect of curve alignment and functional PCA

AU - Park, Juhyun

N1 - The original publication is available at www.link.springer.com

PY - 2008

Y1 - 2008

N2 - When dealing with multiple curves as functional data, it is a common practice to apply functional PCA to summarise and characterise random variation infinite dimension. Often functional data however exhibits additional time variability that distorts the assumed common structure. This is recognized as the problem of curve registration. While the registration step is routinely employed, this is considered as a preprocessing step prior to any serious analysis. Consequently, the effect of alignment is mostly ignored in subsequent analyses and is not well understood. We revisit the issue by particularly focusing on the effect of time variability on the FPCA and illustrate the phenomena from a borrowed perturbation viewpoint. The analysis further suggests an iterative estimating procedure to optimise FPCA.

AB - When dealing with multiple curves as functional data, it is a common practice to apply functional PCA to summarise and characterise random variation infinite dimension. Often functional data however exhibits additional time variability that distorts the assumed common structure. This is recognized as the problem of curve registration. While the registration step is routinely employed, this is considered as a preprocessing step prior to any serious analysis. Consequently, the effect of alignment is mostly ignored in subsequent analyses and is not well understood. We revisit the issue by particularly focusing on the effect of time variability on the FPCA and illustrate the phenomena from a borrowed perturbation viewpoint. The analysis further suggests an iterative estimating procedure to optimise FPCA.

U2 - 10.1007/978-3-7908-2062-1_37

DO - 10.1007/978-3-7908-2062-1_37

M3 - Conference contribution/Paper

SN - 9783790820614

SP - 243

EP - 245

BT - Functional and operatorial statistics

A2 - Dabo-Niang, Sophie

A2 - Ferraty, Frédéric

PB - Springer

ER -