Distance-based Clustering of Functional Data with Derivative Principal Component Analysis-Reference-Cited by-同舟云学术

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Distance-based Clustering of Functional Data with Derivative Principal Component Analysis

Published:2024-09-03 Issue: Volume: Page:1-12
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Yu Ping¹,Shi Gongming²,Wang Chunjie³,Song Xinyuan⁴

Affiliation:

1. School of Mathematics and Computer Science, Shanxi Normal University, Taiyuan, China;

2. School of Statistics, Capital University of Economics and Business, Beijing, China;

3. School of Mathematics and Statistics, Changchun University of Technology, Changchun, China;

4. Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong

Funder

Natural Science Foundation of Shanxi Province

National Statistical Science Research Project of China

Natural Science Foundation of Shanxi normal University

Natural Science Foundation of China

Research Grant Council of HKSAR

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2024.2366499

Reference32 articles.

1. Sparse and smooth functional data clustering

2. Chen Y. Carroll C. Dai X. Fan J. Hadjipantelis P. Z. Han K. Ji H. Müller H. G. and Wang J. L. (2020) fdapace: Functional Data Analysis and Empirical Dynamics R package version 0.5.

3. Functional clustering and identifying substructures of longitudinal data

4. Correlation-Based Functional Clustering via Subspace Projection

5. Derivative Principal Components for Representing the Time Dynamics of Longitudinal and Functional Data

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