Dynamic partially functional linear regression model

  • Jiang DuEmail author
  • Hui Zhao
  • Zhongzhan Zhang
Original Paper


In this paper, we develop a dynamic partially functional linear regression model in which the functional dependent variable is explained by the first order lagged functional observation and a finite number of real-valued variables. The bivariate slope function is estimated by bivariate tensor-product B-splines. Under some regularity conditions, the large sample properties of the proposed estimators are established. We investigate the finite sample performance of the proposed methods via Monte Carlo simulation studies, and illustrate its usefulness by the analysis of the electricity consumption data.


Functional time series Bivariate tensor-product B-splines Functional data analysis Autoregressive Hilbertian process 

Mathematics Subject Classification

62G08 62G20 



Du’s work is supported by the National Natural Science Foundation of China (Nos. 11771032), the Science and Technology Project of Beijing Municipal Education Commission (KM201910005015), Young Talent program of Beijing Municipal Commission of Education (No. CIT&TCD201904021), and the International Research Cooperation Seed Fund of Beijing University of Technology (No. 006000514118553). Zhangs work is supported by the National Natural Science Foundation of China (No. 11271039).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Applied SciencesBeijing University of TechnologyBeijingChina

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