Robust estimators of functional single index models for longitudinal data

Yang Sun and Xiangzhong Fang

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 19, 6869-6890

Abstract: Functional single index model is a powerful model for longitudinal data, which effectively reduces the dimension of covariates. Inspired by robust calibration of computer models, we propose a robust estimator based on Huber loss for functional single index model with fixed dimension parameter. Furthermore, we also consider the case of high dimension parameter, and proposed a robust L1 estimator of the unknown parameter based on adaptive lasso penalty. Theoretical properties including the asymptotic and non asymptotic results have also been investigated. Numerical studies including simulated experiments and an application to AIDS data verify the validity of the proposed estimators.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2023.2253939 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:53:y:2024:i:19:p:6869-6890

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2023.2253939

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().