Robust functional principal component analysis for non-Gaussian longitudinal data

Rou Zhong, Shishi Liu, Haocheng Li and Jingxiao Zhang

Journal of Multivariate Analysis, 2022, vol. 189, issue C

Abstract: Functional principal component analysis is essential in functional data analysis, but the inference will become unconvincing when non-Gaussian characteristics occur (e.g., heavy tail and skewness). The focus of this manuscript is to develop a robust functional principal component analysis methodology to deal with non-Gaussian longitudinal data, where sparsity and irregularity along with non-negligible measurement errors must be considered. We introduce a Kendall’s τ function to handle the non-Gaussian issues. Moreover, the estimation algorithm is studied and the asymptotic theory is discussed. Our method is validated by a simulation study and it is applied to analyze a real world dataset.

Keywords: Functional principal component analysis; Kendall’s τ function; Local polynomial smoother; Longitudinal study; Non-Gaussian (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X21001421
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:189:y:2022:i:c:s0047259x21001421

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2021.104864

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().