Sains Malaysiana 48(9)(2019): 2051–2060

http://dx.doi.org/10.17576/jsm-2019-4809-26

 

Some New Diagnostics of Multicollinearity in Linear Regression Model

(Beberapa Diagnostik Baru Multikekolinearan dalam Model Regresi Linear)

 

MUHAMMAD IMDAD ULLAH1*, MUHAMMAD ASLAM1, SAIMA ALTAF1 & MUNIR AHMED2

 

1Department of Statistics, Bahauddin Zakariya University, Multan 60800, Pakistan

 

2Department of Management Sciences, COMSAT University, Vehari Campus, Islamabad

 

Diserahkan: 21 Oktober 2018/Diterima: 3 Mei 2019

 

ABSTRACT

The problem of multicollinearity compromises the numerical stability of the regression coefficient estimate and cause some serious problem in validation and interpretation of the model. In this paper, we propose two new collinearity diagnostics for the detection of collinearity among regressors, based on coefficient of determination and adjusted coefficient of determination from auxiliary regression of regressors. A Monte Carlo simulation study has been conducted to compare the existing and proposed collinearity diagnostic tests. Comparison of diagnostics on some existing collinear data are also made.

 

Keywords: Collinearity diagnostics; ill-conditioning; linear dependencies; multicollinearity; regression analysis

 

ABSTRAK

Masalah multikekolinearan kompromi kestabilan berangka pekali regresi anggaran dan menyebabkan beberapa masalah serius dalam pengesahan dan tafsiran model. Dalam kajian ini, kami mencadangkan dua diagnostik kekolinearan baru untuk pengesanan kekolinearan dalam kalangan peregrasi, berdasarkan pekali penentuan dan pekali penentuan terlaras daripada bantuan regresi oleh peregrasi. Kajian simulasi Monte Carlo telah dijalankan untuk membandingkan kajian kekolinearan sedia ada dengan cadangan ujian kekolinearan diagnostik. Perbandingan diagnostik pada sesetengah data kolinear sedia ada turut dijalankan.

 

Kata kunci: Analisis regresi; kebergantungan linear; kekolinearan diagnostik; multi-kekolinearan; persuasanaan tak sihat

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*Pengarang untuk surat-menyurat; email: mimdadasad@gmail.com

 

 

 

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