Robust estimation of functional factor models with functional pairwise spatial signs

Shuquan Yang () and Nengxiang Ling ()
Additional contact information
Shuquan Yang: Hefei University of Technology
Nengxiang Ling: Hefei University of Technology

Computational Statistics, 2025, vol. 40, issue 1, No 4, 87-110

Abstract: Abstract Factor model analysis has emerged as a powerful tool to capture the latent dynamic structure of functional data from a dimension-reduction viewpoint. Conventional methods for estimating the factor model are sensitive to heavy tails and outliers. To address this issue and achieve robustness, we provide an eigenvalue-ratio based method to estimate the number of factors by replacing the covariance operator with the functional pairwise spatial sign operator. Moreover, we propose a two-step robust approach to recover the factor space. The convergence rates of the robust estimators for factor loadings, factor scores, and common components are derived under some mild conditions. Numerical studies and a real data analysis confirm the proposed procedures remain reliable even when the factors and idiosyncratic errors have heavy-tailed distributions.

Keywords: Functional data analysis; Functional factor model; Functional pairwise spatial sign; Robust estimation (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-024-01477-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:compst:v:40:y:2025:i:1:d:10.1007_s00180-024-01477-2

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-024-01477-2

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().