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Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.]

Inaccurate and invalid statistical inferences in regression analysis may be caused by multicollinearity due to the presence of high leverage points (HLP) in a data set. Therefore, it is important that high leverage point which is a form ofoutlier be detected because its existence can lead to misfitt...

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Main Authors: Mohamed Ramli, Norazan, Mahmud, Zamalia, Zakaria, Husein, Idris, Mohammad Radzi, Abdul Aziz, Alizan
Format: Article
Language:English
Published: Research Management Institute (RMI) 2010
Subjects:
Managerial accounting
Balance sheet. Financial statements. Corporation reports. Including pro forma statements
Online Access:https://ir.uitm.edu.my/id/eprint/13085/2/13085.pdf
https://ir.uitm.edu.my/id/eprint/13085/
https://smrj.uitm.edu.my/
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spelling my.uitm.ir.130852023-04-10T07:21:02Z https://ir.uitm.edu.my/id/eprint/13085/ Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.] smrj Mohamed Ramli, Norazan Mahmud, Zamalia Zakaria, Husein Idris, Mohammad Radzi Abdul Aziz, Alizan Managerial accounting Balance sheet. Financial statements. Corporation reports. Including pro forma statements Inaccurate and invalid statistical inferences in regression analysis may be caused by multicollinearity due to the presence of high leverage points (HLP) in a data set. Therefore, it is important that high leverage point which is a form ofoutlier be detected because its existence can lead to misfitting of a regression model, thus resulting in inaccuracy of regression results. In this paper, several methods have been proposed to identify HLP in a financial accounting data set prior to conducting further analysis of regression and other multivariate analysis. The Pearson scorrelation coefficient and variance inflation factors (VIF) were used to measure the success of a detection method. Numerical analysis showed that common diagnostics like the twice-mean and thrice-mean rules failed to detect HLP in the given data set whilst robust approaches such as the potentials and diagnostic-robust generalized potentials (DRGP) methods were found to be successful in identifying high leverage point as indicated by lower values of the Pearson s correlation coefficient and variance inflation factors. Research Management Institute (RMI) 2010 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/13085/2/13085.pdf Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.]. (2010) Social and Management Research Journal (SMRJ), 7 (2). pp. 17-30. ISSN 1675-7017 https://smrj.uitm.edu.my/
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Managerial accounting
Balance sheet. Financial statements. Corporation reports. Including pro forma statements
spellingShingle Managerial accounting
Balance sheet. Financial statements. Corporation reports. Including pro forma statements
Mohamed Ramli, Norazan
Mahmud, Zamalia
Zakaria, Husein
Idris, Mohammad Radzi
Abdul Aziz, Alizan
Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.]
description Inaccurate and invalid statistical inferences in regression analysis may be caused by multicollinearity due to the presence of high leverage points (HLP) in a data set. Therefore, it is important that high leverage point which is a form ofoutlier be detected because its existence can lead to misfitting of a regression model, thus resulting in inaccuracy of regression results. In this paper, several methods have been proposed to identify HLP in a financial accounting data set prior to conducting further analysis of regression and other multivariate analysis. The Pearson scorrelation coefficient and variance inflation factors (VIF) were used to measure the success of a detection method. Numerical analysis showed that common diagnostics like the twice-mean and thrice-mean rules failed to detect HLP in the given data set whilst robust approaches such as the potentials and diagnostic-robust generalized potentials (DRGP) methods were found to be successful in identifying high leverage point as indicated by lower values of the Pearson s correlation coefficient and variance inflation factors.
format Article
author Mohamed Ramli, Norazan
Mahmud, Zamalia
Zakaria, Husein
Idris, Mohammad Radzi
Abdul Aziz, Alizan
author_facet Mohamed Ramli, Norazan
Mahmud, Zamalia
Zakaria, Husein
Idris, Mohammad Radzi
Abdul Aziz, Alizan
author_sort Mohamed Ramli, Norazan
title Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.]
title_short Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.]
title_full Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.]
title_fullStr Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.]
title_full_unstemmed Assessing multicollinearity via identification of high leverage points in financial accounting data / Norazan Mohamed Ramli ... [et al.]
title_sort assessing multicollinearity via identification of high leverage points in financial accounting data / norazan mohamed ramli ... [et al.]
publisher Research Management Institute (RMI)
publishDate 2010
url https://ir.uitm.edu.my/id/eprint/13085/2/13085.pdf
https://ir.uitm.edu.my/id/eprint/13085/
https://smrj.uitm.edu.my/
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score 13.211869

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