Estimations of Kibria-Lukman and Lui to identify the most important variables influencing COVID-19
DOI:
https://doi.org/10.31150/ajebm.v5i3.965Keywords:
Multicolinearity, OLS, Kibria-Lukman estimator, Lui estimatorAbstract
The problem of Multicollinearity between explanatory variables is one of the problems that have attracted the attention of many researchers and that the ordinary least squares method is unable to solve it , so the researchers found solutions to this problem by using the principal components method, the ridge regression method, or the lasso regression method, but these methods are not accurate enough to obtain on efficient estimates of the multiple regression model, especially in the presence of the problem of Multicollinearity. In this research, the capabilities of Kibria-Lukman and Lui will be used to determine the most important factors affecting infection with the Covid-19 virus among the variables that suffer from the problem of Multicollinearity to solve this problem and then obtain the best variables that represent the most important factors affecting infection with the virus. It was concluded that the Kibria-Lukman estimator is better than the rest of the estimators because it achieved the least mean squares error (MSE), and was able to solve the problem of Multicollinearity among the explanatory variables, followed by the Liu estimator and then the Ridge regression estimator, and finally the ordinary least squares estimator that was unable to solve the problem of linear multiplicity.
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Al-Quraishi, Zainab Kazem Mezher, (2018), “Comparing the regression estimators of crafts, major compounds and partial least squares in the regression model”, a master’s thesis, College of Administration and Economics, University of Karbala.
Gujarati, Damodar, authored, Odeh, Hind Abdul Ghaffar and Al-Dash, Afaf Ali Hassan, (2015), "Econometrics", Marrakesh Publishing House, Saudi Arabia, Part One.
Gabriel, Mohamed Suleiman Mohamed (2014), “Polylinearity, its causes, effects, and treatment with ridge slope and principal component regression with application to hypothetical data”, PhD thesis, Republic of Sudan, Sudan University of Science and Technology, College of Postgraduate Studies, sudan
Hoerl, A.E. and Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Non-Orthogonal Problems. Technometrics, 12, 55-67.
F. Lukman, K. Ayinde, S. Binuomote, and O. A. Clement, “Modified ridge-type estimator to combat multicollinearity:
F. Swindel, “Good ridge estimators based on prior information,” Communications in Statistics-0eory and Methods,
M. Golam Kibria & Adewale F. Lukman, (2020), " A New Ridge-Type Estimator for the Linear Regression Model:
Berkley Symposium on Mathematical and Statistics Probability, J. Neyman, Ed., vol. 1, pp. 197–206, Springer, Berlin, Germany, 1956.
C. Stein, “Inadmissibility of the usual estimator for mean of multivariate normal distribution,” in Proceedings of the 0ird
El-Dereny and N. I. Rashwan, " Solving Multicollinearity ProblemUsing Ridge Regression Models" Int. J. Contemp. Math. Sciences, Vol. 6, 2011, no. 12, 585 – 600
Donald E. Farrar and Robert R. Glauber, 1967, "Multicollinearity in Regression Analysis: The Problem Revisited", The Review of Economics and Statistics Vol. 49, No. 1 (Feb., 1967), pp. 92-107 (16 pages) Published By: The MIT Press https://doi.org/10.2307/1937887 https://www.jstor.org/stable/1937887
F. Akdeniz and S. Kaçiranlar, “On the almost unbiased generalized liu estimator and unbiased estimation of the bias
H. Yang and X. Chang, “A new two-parameter estimator in linear regression,” Communications in Statistics-0eory and
Hoerl, Arthur .E. and Kennard, Robert W., (1970a)."Ridge regression: Biased estimation for non-orthogonal Problems", T Econometrics Journal , Vol.12, No.1,55-67
JOSEPH , AKINNIYI, ALABA ; SANNI, ENEJI ADEMOH, (2017), " THE FARRAR-GLAUBAR APPROACH IN TESTING FOR MULTICOLLINEARITY IN ECONOMIC DATA", International Journal for Research in Business, Management and Accounting ISSN: 2455-6114, Volume 02 Issue 01
K. Liu, “A new class of biased estimate in linear regression,” Communication in Statistics- 0eory and Methods, vol. 22,
Rom´an Salmer´on-G´omez, Catalina B. Garc´ıa-Garc´ıa and Jos´e Garc´ıa-P´erez, (2020), " Overcoming the inconsistences of the variance inflation factor: a redefined VIF and a test to detect statistical troubling multicollinearity." , ectaart.cls ver. 2006/04/11 file: Manuscript_econometrika_def.tex date: May 6, 2020
S. Sakallıo˘glu and S. Kaçıranlar, “A new biased estimator based on ridge estimation,” Statistical Papers, vol. 49, no. 4,
Shrestha, Noora, (2020), " Detecting Multicollinearity in Regression Analysis " , American Journal of Applied Mathematics and Statistics, 2020, Vol. 8, No. 2, 39-42 Available online at http://pubs.sciepub.com/ajams/8/2/1 Published by Science and Education Publishing DOI:10.12691/ajams-8-2-1
Simulations and Applications", Hindawi Scientifica Volume 2020, Article ID 9758378, 16 pages https://doi.org/10.1155/2020/9758378 vol. 12, no. 1, pp. 55–67, 1970. vol. 5, no. 11, pp. 1065–1075, 1976.
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Copyright (c) 2022 Namariq Qassim Hussain, Zainab Kadhum Mezher, Mahdi Wahab Neama
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