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

Ping Yu, Ting Li, Zhongyi Zhu () and Zhongzhan Zhang
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Ping Yu: Fudan University
Ting Li: Fudan University
Zhongyi Zhu: Fudan University
Zhongzhan Zhang: Beijing University of Technology

Metrika: International Journal for Theoretical and Applied Statistics, 2019, vol. 82, issue 6, No 1, 633-656

Abstract: Abstract 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.

Keywords: Composite quantile estimation; Functional principal component analysis; Functional linear regression model; Short-range dependence; Strictly stationary; 62G08; 62G20 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00184-018-0699-3

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