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 L 1 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.

Full Text

Published Version
Open DOI Link

Get access to 115M+ research papers

Discover from 40M+ Open access, 2M+ Pre-prints, 9.5M Topics and 32K+ Journals.

Sign Up Now! It's FREE

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call