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:: Volume 27, Issue 2 (3-2023) ::
Andishe 2023, 27(2): 41-52 Back to browse issues page
‎Modelling of ‎functional data ‎using‎ principal component regression approach based on the generalized cross validation criterion
Mahdi Roozbeh *
Semnan University
Abstract:   (279 Views)

Functional data analysis is used to develop statistical approaches to the data sets that are functional and continuous essentially‎, ‎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 ‎proposed ‎methods, ‎the generalized cross validation, which is a ‎valid ‎and ‎efficient ‎criterion, ‎is ‎applied.‎

Keywords: ‎Functional Data Analysis, Functional Regression, Generalized Cross ‎Validation, ‎‎Principal Component Regression.
Full-Text [PDF 288 kb]   (286 Downloads)    
Type of Study: Research | Subject: Special
Received: 2021/07/3 | Accepted: 2023/05/19 | Published: 2023/05/19
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Roozbeh M. ‎Modelling of ‎functional data ‎using‎ principal component regression approach based on the generalized cross validation criterion. Andishe 2023; 27 (2) :41-52
URL: http://andisheyeamari.irstat.ir/article-1-857-en.html

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Volume 27, Issue 2 (3-2023) Back to browse issues page
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