Handling Critical Multicollinearity Using Parametric Approach

Obubu Maxwell*, George Amaeze Osuji, Ibeakuzie Precious Onyedikachi, Chinelo Ijeoma Obi-Okpala, Ikediuwa Udoka Chinedu and Okpala Ikenna Frank
Additional contact information
Obubu Maxwell*: Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
George Amaeze Osuji: Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
Ibeakuzie Precious Onyedikachi: Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
Chinelo Ijeoma Obi-Okpala: Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
Ikediuwa Udoka Chinedu: Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria
Okpala Ikenna Frank: Department of Statistics, Nnamdi Azikiwe University, Awka, Nigeria

Academic Journal of Applied Mathematical Sciences, 2019, vol. 5, issue 11, 150-163

Abstract: In regression analysis, it is relatively necessary to have a correlation between the response and explanatory variables, but having correlations amongst explanatory variables is something undesired. This paper focuses on five methodologies for handling critical multicollinearity, they include: Partial Least Square Regression (PLSR), Ridge Regression (RR), Ordinary Least Square Regression (OLS), Least Absolute Shrinkage and Selector Operator (LASSO) Regression, and the Principal Component Analysis (PCA). Monte Carlo Simulations comparing the methods was carried out with the sample size greater than or equal to the levels (n>p) considered in most cases, the Average Mean Square Error (AMSE) and Akaike Information Criterion (AIC) values were computed. The result shows that PCR is the most superior and more efficient in handling critical multicollinearity problems, having the lowest AMSE and AIC values for all the sample sizes and different levels considered.

Keywords: Multicollinearity; Least absolute shrinkage and selection operator; Partial least square regression; Akaike information criterion; Average mean square error; Principal component analysis; Ordinary least square regression; Ridge rsegression. (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.arpgweb.com/pdf-files/ajams5(11)150-163.pdf (application/pdf)
https://www.arpgweb.com/journal/17/archive/11-2019/11/5 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arp:ajoams:2019:p:150-163

DOI: 10.32861/ajams.511.150.163

Access Statistics for this article

Academic Journal of Applied Mathematical Sciences is currently edited by Dr. Diana Bílková

More articles in Academic Journal of Applied Mathematical Sciences from Academic Research Publishing Group Rahim Yar Khan 64200, Punjab, Pakistan.
Bibliographic data for series maintained by Managing Editor ().