Functional Data Visualization

Marc G. Genton; Ying Sun

doi:10.1002/9781118445112.stat08290

Functional Data Visualization

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This article reviews tools to visualize functional data, that is, curves, surfaces/images, and trajectories. These tools are based on ranking functional data by means of notions of depth/outlyingness and make use of methods for functional outlier detections. For univariate functional data, the functional boxplot and surface boxplot are emphasized. For multivariate functional data, the magnitude–shape plot, the two-stage functional boxplot, and the trajectory functional boxplot are described. A bivariate functional dataset of the angles formed by the hip and knee of 39 children over their gait cycles is used throughout for illustration of the various visualization tools.

Original language	English (US)
Title of host publication	Wiley StatsRef: Statistics Reference Online
Publisher	Wiley
Pages	1-11
Number of pages	11
ISBN (Print)	9781118445112
DOIs	https://doi.org/10.1002/9781118445112.stat08290
State	Published - Nov 4 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-03-02
Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST).

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10.1002/9781118445112.stat08290

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title = "Functional Data Visualization",

abstract = "This article reviews tools to visualize functional data, that is, curves, surfaces/images, and trajectories. These tools are based on ranking functional data by means of notions of depth/outlyingness and make use of methods for functional outlier detections. For univariate functional data, the functional boxplot and surface boxplot are emphasized. For multivariate functional data, the magnitude–shape plot, the two-stage functional boxplot, and the trajectory functional boxplot are described. A bivariate functional dataset of the angles formed by the hip and knee of 39 children over their gait cycles is used throughout for illustration of the various visualization tools.",

author = "Genton, {Marc G.} and Ying Sun",

note = "KAUST Repository Item: Exported on 2021-03-02 Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST).",

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doi = "10.1002/9781118445112.stat08290",

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AU - Sun, Ying

N1 - KAUST Repository Item: Exported on 2021-03-02 Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST).

PY - 2020/11/4

Y1 - 2020/11/4

N2 - This article reviews tools to visualize functional data, that is, curves, surfaces/images, and trajectories. These tools are based on ranking functional data by means of notions of depth/outlyingness and make use of methods for functional outlier detections. For univariate functional data, the functional boxplot and surface boxplot are emphasized. For multivariate functional data, the magnitude–shape plot, the two-stage functional boxplot, and the trajectory functional boxplot are described. A bivariate functional dataset of the angles formed by the hip and knee of 39 children over their gait cycles is used throughout for illustration of the various visualization tools.

AB - This article reviews tools to visualize functional data, that is, curves, surfaces/images, and trajectories. These tools are based on ranking functional data by means of notions of depth/outlyingness and make use of methods for functional outlier detections. For univariate functional data, the functional boxplot and surface boxplot are emphasized. For multivariate functional data, the magnitude–shape plot, the two-stage functional boxplot, and the trajectory functional boxplot are described. A bivariate functional dataset of the angles formed by the hip and knee of 39 children over their gait cycles is used throughout for illustration of the various visualization tools.

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SP - 1

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BT - Wiley StatsRef: Statistics Reference Online

PB - Wiley

ER -

Functional Data Visualization

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