Ding, Hui, Zhang, Riquan, Zhang, Jian (2018) Quantile Estimation for a Hybrid Model of Functional and Varying Coefficient Regressions. Journal of Statistical Planning and Inference, 196 . pp. 1-18. ISSN 0378-3758. (doi:10.1016/j.jspi.2017.10.005) (KAR id:64230)
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Official URL: https://doi.org/10.1016/j.jspi.2017.10.005 |
Abstract
We consider a hybrid of functional and varying-coefficient regression models for the analysis of mixed functional data. We propose a quantile estimation of this hybrid model as an alternative to the least square approach. Under regularity conditions, we establish the asymptotic normality of the proposed estimator. We show that the estimated slope function can attain the minimax convergence rate as in functional linear regression. A Monte Carlo simulation study and a real data application suggest that the proposed estimation is promising.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.jspi.2017.10.005 |
Uncontrolled keywords: | Functional data analysis, varying coefficient, partially functional regression, convergence rate, mixed data |
Subjects: | Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Funders: | National Natural Science Foundation of China (https://ror.org/01h0zpd94) |
Depositing User: | Jian Zhang |
Date Deposited: | 03 Nov 2017 12:23 UTC |
Last Modified: | 04 Mar 2024 19:17 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/64230 (The current URI for this page, for reference purposes) |
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