Composite Quantile Estimation in Partial Functional Linear Regression Model Based on Polynomial Spline

In this paper, we consider composite quantile regression for partial functional linear regression model with polynomial spline approximation. Under some mild conditions, the convergence rates of the estimators and mean squared prediction error, and asymptotic normality of parameter vector are obtain...

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Bibliographic Details
Published inActa mathematica Sinica. English series Vol. 37; no. 10; pp. 1627 - 1644
Main Authors Yu, Ping, Li, Ting, Zhu, Zhong Yi, Shi, Jian Hong
Format Journal Article
LanguageEnglish
Published Beijing Institute of Mathematics, Chinese Academy of Sciences and Chinese Mathematical Society 01.10.2021
Springer Nature B.V
Subjects
Mathematics
Mathematics and Statistics
Normality
Outliers (statistics)
Polynomials
Random errors
Regression analysis
Regression models
62G05
Asymptotic normality
functional data analysis
62G20
composite quantile regression
polynomial spline
62G35
rates of convergence
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Summary:In this paper, we consider composite quantile regression for partial functional linear regression model with polynomial spline approximation. Under some mild conditions, the convergence rates of the estimators and mean squared prediction error, and asymptotic normality of parameter vector are obtained. Simulation studies demonstrate that the proposed new estimation method is robust and works much better than the least-squares based method when there are outliers in the dataset or the random error follows heavy-tailed distributions. Finally, we apply the proposed methodology to a spectroscopic data sets to illustrate its usefulness in practice.
ISSN:1439-8516
1439-7617
DOI:10.1007/s10114-021-9172-8