Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]

Ordinary Least Squares (OLS) estimator become worse in the presence of multicollinearity and outlier. Here, three methods are suggested to improve the model when multicollinearity and outlier exists, the first one is Jackknife Regression (JR) based on left out method, the second is Ridge Regression...

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Main Authors:	Jamil, Aida Nurasikin, Abdul Muluk, Muhammad Fahmi, Anuar, Nur Sabrina, Abu Bakar, Mohamad Suffian
Format:	Thesis
Language:	English
Published:	2019
Subjects:	H Social Sciences (General)
Online Access:	https://ir.uitm.edu.my/id/eprint/32559/1/32559.pdf
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id	my-uitm-ir.32559
record_format	uketd_dc
spelling	my-uitm-ir.325592020-07-16T07:19:09Z Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.] 2019-07 Jamil, Aida Nurasikin Abdul Muluk, Muhammad Fahmi Anuar, Nur Sabrina Abu Bakar, Mohamad Suffian H Social Sciences (General) Study and teaching. Research Ordinary Least Squares (OLS) estimator become worse in the presence of multicollinearity and outlier. Here, three methods are suggested to improve the model when multicollinearity and outlier exists, the first one is Jackknife Regression (JR) based on left out method, the second is Ridge Regression (RR) based on the addition of shrinking parameter, and the third is Latent Root Regression (LRR) by adding the latent root and latent vector. In the application, model parameters, standard errors, length of confidence intervals (L.C.I), coefficients of determination ( 2 R ), and mean square error (MSE) of these methods are estimated. Next, the perfomance of these three methods are compared with OLS by using the MSE and 2 R .Based on the analysis, LRR method was the best method compared to other methods since the value of MSE is less and 2 R is higher among others. The LRR was not only the best method when multicollinearity exist, but also was the best when the presence of both multicollinearity and outlier 2019-07 Thesis https://ir.uitm.edu.my/id/eprint/32559/ https://ir.uitm.edu.my/id/eprint/32559/1/32559.pdf text en public degree Universiti Teknologi MARA, Kelantan Faculty of Computer and Mathematical Sciences Ab. Aziz, Nasuhar
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ab. Aziz, Nasuhar
topic	H Social Sciences (General) H Social Sciences (General)
spellingShingle	H Social Sciences (General) H Social Sciences (General) Jamil, Aida Nurasikin Abdul Muluk, Muhammad Fahmi Anuar, Nur Sabrina Abu Bakar, Mohamad Suffian Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]
description	Ordinary Least Squares (OLS) estimator become worse in the presence of multicollinearity and outlier. Here, three methods are suggested to improve the model when multicollinearity and outlier exists, the first one is Jackknife Regression (JR) based on left out method, the second is Ridge Regression (RR) based on the addition of shrinking parameter, and the third is Latent Root Regression (LRR) by adding the latent root and latent vector. In the application, model parameters, standard errors, length of confidence intervals (L.C.I), coefficients of determination ( 2 R ), and mean square error (MSE) of these methods are estimated. Next, the perfomance of these three methods are compared with OLS by using the MSE and 2 R .Based on the analysis, LRR method was the best method compared to other methods since the value of MSE is less and 2 R is higher among others. The LRR was not only the best method when multicollinearity exist, but also was the best when the presence of both multicollinearity and outlier
format	Thesis
qualification_level	Bachelor degree
author	Jamil, Aida Nurasikin Abdul Muluk, Muhammad Fahmi Anuar, Nur Sabrina Abu Bakar, Mohamad Suffian
author_facet	Jamil, Aida Nurasikin Abdul Muluk, Muhammad Fahmi Anuar, Nur Sabrina Abu Bakar, Mohamad Suffian
author_sort	Jamil, Aida Nurasikin
title	Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]
title_short	Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]
title_full	Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]
title_fullStr	Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]
title_full_unstemmed	Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]
title_sort	comparison of parameter estimation methods when multicollinearity and outlier exists / aida nurasikin jamil ...[et al.]
granting_institution	Universiti Teknologi MARA, Kelantan
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/32559/1/32559.pdf
_version_	1783734174571560960

Comparison of parameter estimation methods when multicollinearity and outlier exists / Aida Nurasikin Jamil ...[et al.]

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