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

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Beyaztas U., Shang H. L.

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

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s10651-019-00436-1
  • Dergi Adı: ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Environment Index, Geobase, Greenfile, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.95-114
  • Anahtar Kelimeler: Basis function, Functional data, NIPALS, Nonparametric smoothing, SIMPLS, VARYING-COEFFICIENT MODELS, PRINCIPAL COMPONENT REGRESSION, LINEAR-REGRESSION, SELECTION
  • Marmara Üniversitesi Adresli: Hayır

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.