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Dissimilarity for functional data clustering based on smoothing parameter commutation

Tzeng, S; Hennig, C; Li, Y-F; Lin, C-J; (2018) Dissimilarity for functional data clustering based on smoothing parameter commutation. Statistical Methods in Medical Research , 27 (11) pp. 3492-3504. 10.1177/0962280217710050. Green open access

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Abstract

Many studies measure the same type of information longitudinally on the same subject at multiple time points, and clustering of such functional data has many important applications. We propose a novel and easy method to implement dissimilarity measure for functional data clustering based on smoothing splines and smoothing parameter commutation. This method handles data observed at regular or irregular time points in the same way. We measure the dissimilarity between subjects based on varying curve estimates with pairwise commutation of smoothing parameters. The intuition is that smoothing parameters of smoothing splines reflect the inverse of the signal-to-noise ratios and that when applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. Our method takes into account the estimation uncertainty using smoothing parameter commutation and is not strongly affected by outliers. It can also be used for outlier detection. The effectiveness of our proposal is shown by simulations comparing it to other dissimilarity measures and by a real application to methadone dosage maintenance levels.

Type: Article
Title: Dissimilarity for functional data clustering based on smoothing parameter commutation
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/0962280217710050
Publisher version: https://doi.org/10.1177/0962280217710050
Language: English
Additional information: © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/).
Keywords: Clustering, dissimilarity, functional data, irregular longitudinal data, outliers, smoothing splines
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10045983
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