Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition

In this study, the penalized regularization methods, namely, the smoothly clipped absolute deviation (SCAD), adaptive least absolute shrinkage and selection operator (adLASSO) regression, minimax concave penalty (MCP) and elastic net (ELNET) regression, are adopted. Those methods are combined with t...

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Main Author:	Al Jawarneh, Abdullah Suleiman Saleh
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA1 Mathematics (General)
Online Access:	http://eprints.usm.my/51620/1/ABDULLAH%20SULEIMAN%20SALEH%20AL%20JAWARNEH%20-%20TESIS.pdf
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spelling	my-usm-ep.516202022-02-23T03:25:26Z Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition 2021-02 Al Jawarneh, Abdullah Suleiman Saleh QA1 Mathematics (General) In this study, the penalized regularization methods, namely, the smoothly clipped absolute deviation (SCAD), adaptive least absolute shrinkage and selection operator (adLASSO) regression, minimax concave penalty (MCP) and elastic net (ELNET) regression, are adopted. Those methods are combined with the first part of the Hilbert–Huang transformation, namely, the empirical mode decomposition (EMD) algorithm. The EMD algorithm is employed to decompose the nonstationary and nonlinear time series dataset into a finite set of orthogonal decomposition components, which includes a set of intrinsic mode function and residual components. These components have been used in several studies as new predictor variables to predict the behaviour of the response variable. The penalized regularization methods are statistical techniques used to regularize and select the necessary predictor variables that have substantial effects on the response variable. These methods are also utilized to produce a consistent model in terms of variable selection and asymptotically normal estimates and address the multicollinearity problem when it exists between the predictor variables. This study aims to apply the proposed SCAD-EMD, adLASSO-EMD, MCP-EMD and ELNET-EMD methods to determine the effect of the decomposition components of the original univariate/multivariate time series predictor variable(s) on the response variable. Moreover, this study tackles the multicollinearity between the decomposition components to enhance the prediction accuracy for creating a fitting model. The proposed techniques are compared with four traditional regression methods employed in the previous study. 2021-02 Thesis http://eprints.usm.my/51620/ http://eprints.usm.my/51620/1/ABDULLAH%20SULEIMAN%20SALEH%20AL%20JAWARNEH%20-%20TESIS.pdf application/pdf en public phd doctoral Perpustakaan Hamzah Sendut Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1 Mathematics (General)
spellingShingle	QA1 Mathematics (General) Al Jawarneh, Abdullah Suleiman Saleh Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition
description	In this study, the penalized regularization methods, namely, the smoothly clipped absolute deviation (SCAD), adaptive least absolute shrinkage and selection operator (adLASSO) regression, minimax concave penalty (MCP) and elastic net (ELNET) regression, are adopted. Those methods are combined with the first part of the Hilbert–Huang transformation, namely, the empirical mode decomposition (EMD) algorithm. The EMD algorithm is employed to decompose the nonstationary and nonlinear time series dataset into a finite set of orthogonal decomposition components, which includes a set of intrinsic mode function and residual components. These components have been used in several studies as new predictor variables to predict the behaviour of the response variable. The penalized regularization methods are statistical techniques used to regularize and select the necessary predictor variables that have substantial effects on the response variable. These methods are also utilized to produce a consistent model in terms of variable selection and asymptotically normal estimates and address the multicollinearity problem when it exists between the predictor variables. This study aims to apply the proposed SCAD-EMD, adLASSO-EMD, MCP-EMD and ELNET-EMD methods to determine the effect of the decomposition components of the original univariate/multivariate time series predictor variable(s) on the response variable. Moreover, this study tackles the multicollinearity between the decomposition components to enhance the prediction accuracy for creating a fitting model. The proposed techniques are compared with four traditional regression methods employed in the previous study.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Al Jawarneh, Abdullah Suleiman Saleh
author_facet	Al Jawarneh, Abdullah Suleiman Saleh
author_sort	Al Jawarneh, Abdullah Suleiman Saleh
title	Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition
title_short	Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition
title_full	Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition
title_fullStr	Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition
title_full_unstemmed	Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition
title_sort	enhancing model selection based on penalized regression methods and empirical mode decomposition
granting_institution	Perpustakaan Hamzah Sendut
granting_department	Pusat Pengajian Sains Matematik
publishDate	2021
url	http://eprints.usm.my/51620/1/ABDULLAH%20SULEIMAN%20SALEH%20AL%20JAWARNEH%20-%20TESIS.pdf
_version_	1747822089517137920

Enhancing Model Selection Based On Penalized Regression Methods And Empirical Mode Decomposition

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