On function-on-function regression: partial least squares approach


Creative Commons License

Beyaztas U. , Shang H. L.

ENVIRONMENTAL AND ECOLOGICAL STATISTICS, cilt.27, sa.1, ss.95-114, 2020 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 27 Konu: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s10651-019-00436-1
  • Dergi Adı: ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • Sayfa Sayıları: ss.95-114

Özet

Functional data analysis tools, such as function-on-function regression models, have received considerable attention in various scientific fields because of their observed high-dimensional and complex data structures. Several statistical procedures, including least squares, maximum likelihood, and maximum penalized likelihood, have been proposed to estimate such function-on-function regression models. However, these estimation techniques produce unstable estimates in the case of degenerate functional data or are computationally intensive. To overcome these issues, we proposed a partial least squares approach to estimate the model parameters in the function-on-function regression model. In the proposed method, the B-spline basis functions are utilized to convert discretely observed data into their functional forms. Generalized cross-validation is used to control the degrees of roughness. The finite-sample performance of the proposed method was evaluated using several Monte-Carlo simulations and an empirical data analysis. The results reveal that the proposed method competes favorably with existing estimation techniques and some other available function-on-function regression models, with significantly shorter computational time.