1
Faculty of Statistics, Semnan University, Semnan, Iran
2
Master's degree in Statistics, Semnan University, Semnan, Iran
Abstract
Functional data analysis is used to develop statistical approaches to the data sets that are functional and continuous es sentially, and because these functions belong to the spaces with infinite dimensional, using conventional methods in classical statistics for analyzing such data sets is challenging. The most popular technique for statistical data analysis is the functional principal components approach, which is an important tool for dimensional reduction. In this research, using the method of functional principal component regression based on the second derivative penalty, ridge and lasso, the analysis of Canadian climate and spectrometric data sets is proceed. To do this, to obtain the optimum values of the penalized parameter in pro posed methods, the generalized cross validation, which is a valid and efficient criterion, is applied.
Roozbeh, M., Roohi, A. & Jihadi, F. (2023). Modelling of functional data using principal component regression approach
based on the generalized cross validation criterion. Andishe_ye Amari, 27(2), 41-52.
MLA
Roozbeh, M., Roohi, A., & Jihadi, F. "Modelling of functional data using principal component regression approach
based on the generalized cross validation criterion", Andishe_ye Amari, 27, 2, 2023, 41-52.
HARVARD
Roozbeh M., Roohi A., Jihadi F. (2023). 'Modelling of functional data using principal component regression approach
based on the generalized cross validation criterion', Andishe_ye Amari, 27(2), pp. 41-52.
CHICAGO
M. Roozbeh, A. Roohi & F. Jihadi, "Modelling of functional data using principal component regression approach
based on the generalized cross validation criterion," Andishe_ye Amari, 27 2 (2023): 41-52,
VANCOUVER
Roozbeh M., Roohi A., Jihadi F. Modelling of functional data using principal component regression approach
based on the generalized cross validation criterion. Andishe_ye Amari. 2023;27(2):41-52 (In Persian).
