Quantile Estimation for a Hybrid Model of Functional and Varying Coefficient Regressions

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)

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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
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
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 03 Nov 2017 12:23 UTC
Last Modified: 29 May 2019 19:46 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64230 (The current URI for this page, for reference purposes)
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