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:: Volume 17, Issue 1 (9-2023) ::
JSS 2023, 17(1): 0-0 Back to browse issues page
Analysis of High Dimensional Data Using Development Support Vector Regression, Functional Regression, Ridge and Lasso Regression
Arta Roohi , Fatemeh Jahadi , Mahdi Roozbeh * , Saeed Zalzadeh
Abstract:   (1947 Views)

‎The high-dimensional data analysis using classical regression approaches is not applicable, and the consequences may need to be more accurate.
This study tried to analyze such data by introducing new and powerful approaches such as support vector regression, functional regression, LASSO and ridge regression. On this subject, by investigating two high-dimensional data sets  (riboflavin and simulated data sets) using the suggested approaches, it is progressed to derive the most efficient model based on three criteria (correlation squared, mean squared error and mean absolute error percentage deviation) according to the type of data.

Keywords: Functional regression‎, ‎High dimensional data‎, ‎Lasso regression‎, ‎Ridge regression‎, ‎Support vector regression.
Full-Text [PDF 1338 kb]   (1658 Downloads)    
Type of Study: Research | Subject: Statistical Inference
Received: 2022/04/20 | Accepted: 2023/09/1 | Published: 2023/07/11
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roohi A, jahadi F, Roozbeh M, Zalzadeh S. Analysis of High Dimensional Data Using Development Support Vector Regression, Functional Regression, Ridge and Lasso Regression. JSS 2023; 17 (1)
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 17, Issue 1 (9-2023) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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