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Semi-functional partial linear quantile regression

Ding, Hui, Lu, Zhiping, Zhang, Jian, Zhang, Riquan (2018) Semi-functional partial linear quantile regression. Statistics & Probability Letters, 142 . pp. 92-101. ISSN 0167-7152. (doi:10.1016/j.spl.2018.07.007)

Abstract

Semi-functional partial linear model is a flexible model in which a scalar response is related to both functional covariate and scalar covariates. We propose a quantile estimation of this model as an alternative to the least square approach. We also extend the proposed method to kNN quantile method. Under some regular conditions, we establish the asymptotic normality of quantile estimators of regression coefficient. We also derive the rates of convergence of nonparametric function. Finite-sample performance of our estimation is compared with least square approach via a Monte Carlo simulation study. The simulation results indicate that our method is much more robust than the least square method. A real data example about spectrometric data is used to illustrate that our model and approach are promising.

Item Type: Article
DOI/Identification number: 10.1016/j.spl.2018.07.007
Uncontrolled keywords: Functional data analysis, Partial linear, Quantile regression
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 31 Aug 2018 12:16 UTC
Last Modified: 19 Jul 2019 23:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/68873 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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