221
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Finding hidden structure of sparse longitudinal data via functional Eigenfunctions

&
Pages 1142-1149 | Published online: 06 Feb 2023
 

ABSTRACT

In this research, we are interested in finding the hidden dependence structure of sparse longitudinal data. Finding the hidden dependence structure of sparse longitudinal data is difficult due to the starting and end times being different. We propose that finding the directional dependence structure of the eigenfunctions by sparse functional principal component analysis (FPCA) may be a good alternative solution to find the hidden dependence structure of sparse longitudinal data. To verify this idea, we apply sparse FPCA to simulated data and two real datasets, wage sparse longitudinal data and Korea composite stock price index (KOSPI) high-frequency minute tick data and then apply vine copula and copula dynamic conditional correlation with asymmetric GARCH model to the functional eigenfunctions from FPCA.

JEL CLASSIFICATION:

Acknowledgements

We thank AE and two respected referees for constructive and helpful suggestions which led to substantial improvement in the revised version. This work was supported by a grant from the National Research Foundation of Korea (NRF-2021R1F1A1047952).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Research Foundation of Korea [2021R1F1A1047952]

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 205.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.