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[期刊论文]

Composite quantile estimation in partial functional linear regression model with dependent errors

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作者:

Yu, Ping (Yu, Ping.) | Li, Ting (Li, Ting.) | Zhu, Zhongyi (Zhu, Zhongyi.) | 展开

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Scopus SCIE

摘要:

In this paper, we consider composite quantile estimation for the partial functional linear regression model with errors from a short-range dependent and strictly stationary linear processes. The functional principal component analysis method is employed to estimate the slope function and the functional predictive variable, respectively. Under some regularity conditions, we obtain the optimal convergence rate of the slope function, and the asymptotic normality of the parameter vector. 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 autoregressive error distribution follows a heavy-tailed distribution. Finally, we apply the proposed methodology to electricity consumption data.

关键词:

Short-range dependence Functional principal component analysis Composite quantile estimation Strictly stationary Functional linear regression model

作者机构:

  • [ 1 ] [Yu, Ping]Fudan Univ, Dept Stat, Shanghai 200433, Peoples R China
  • [ 2 ] [Li, Ting]Fudan Univ, Dept Stat, Shanghai 200433, Peoples R China
  • [ 3 ] [Zhu, Zhongyi]Fudan Univ, Dept Stat, Shanghai 200433, Peoples R China
  • [ 4 ] [Yu, Ping]Shanxi Normal Univ, Sch Math & Comp Sci, Linfen 041000, Peoples R China
  • [ 5 ] [Zhang, Zhongzhan]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

通讯作者信息:

  • [Zhu, Zhongyi]Fudan Univ, Dept Stat, Shanghai 200433, Peoples R China

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来源 :

METRIKA

ISSN: 0026-1335

年份: 2019

期: 6

卷: 82

页码: 633-656

0 . 7 0 0

JCR@2022

ESI学科: MATHEMATICS;

ESI高被引阀值:54

被引次数:

WoS核心集被引频次: 11

SCOPUS被引频次: 8

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