Rights statement: The original publication is available at www.link.springer.com
Accepted author manuscript, 77.9 KB, PDF document
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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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 -