A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

Kibria, Golam; Lukman, Adewale F.

doi:10.1155/2020/9758378

Cited by 135 publications

(122 citation statements)

References 32 publications

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“…Therefore, we conducted a formal test called variance inflation factor to ascertain if there is multicollinearity problem in the model ( Lukman et al., 2020 ). According to Kibria and Lukman (2020) , when the variance inflation factor (VIF) is greater than ten (10) then there is multicollinearity. However, the result in Table 4 shows that the model does not exhibit multicollinearity problem since VIF is less than 10.…”

Section: Methodsmentioning

confidence: 99%

Predictive modelling of COVID-19 confirmed cases in Nigeria

Ogundokun

Lukman

Kibria

et al. 2020

Infectious Disease Modelling

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The coronavirus outbreak is the most notable world crisis since the Second World War. The pandemic that originated from Wuhan, China in late 2019 has affected all the nations of the world and triggered a global economic crisis whose impact will be felt for years to come. This necessitates the need to monitor and predict COVID-19 prevalence for adequate control. The linear regression models are prominent tools in predicting the impact of certain factors on COVID-19 outbreak and taking the necessary measures to respond to this crisis. The data was extracted from the NCDC website and spanned from March 31, 2020 to May 29, 2020. In this study, we adopted the ordinary least squares estimator to measure the impact of travelling history and contacts on the spread of COVID-19 in Nigeria and made a prediction. The model was conducted before and after travel restriction was enforced by the Federal government of Nigeria. The fitted model fitted well to the dataset and was free of any violation based on the diagnostic checks conducted. The results show that the government made a right decision in enforcing travelling restriction because we observed that travelling history and contacts made increases the chances of people being infected with COVID-19 by 85% and 88% respectively. This prediction of COVID-19 shows that the government should ensure that most travelling agency should have better precautions and preparations in place before re-opening.

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Section: Methodsmentioning

confidence: 99%

Predictive modelling of COVID-19 confirmed cases in Nigeria

Ogundokun

Lukman

Kibria

et al. 2020

Infectious Disease Modelling

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“…However, the estimator is still not efficient when there is multicollinearity (Chatterjee and Price, 1977;Gujarati, 2005; Lukman et al, 2019; Lukman et al, 2020). Among the estimators commonly used to address this problem are the estimator based on Principal Component Regression developed by Massy (1965), the Ridge Regression (RRE) Estimator (Hoerl and Kennard, 1970), estimator based on Partial Least Square (PLS) Regression (Wold, 1985), and Liu estimator (Liu, 1993) and recently, the KL estimator by Kibria and Lukman (2020).…”

Section: Literature Reviewmentioning

confidence: 99%

A New Approach of Principal Component Regression Estimator with Applications to Collinear Data

Ayinde¹,

Lukman²,

Alabi³

et al. 2020

International Journal of Engineering Research and Technology

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In this paper, a new approach to estimating the model parameters in Principal Components (PCs) is developed. Principal Component Analysis (PCA) is a method of variable reduction that has found relevance in combating multicollinearity problem in Multiple Linear Regression Models. In this paper, a new approach to estimating the model parameters in Principal Components (PCs) is developed The method requires using the PCs as regressors to predict the dependent variable and further utilizing the predicted variable as a dependent variable on the original explanatory variables in an Ordinary Least Square (OLS) regression. The resulting parameter estimates are the same as the estimates from the usual Principal Components Regression Estimator. The sampling properties of the new estimator are proved to be same as the existing ones. The new approach is simpler and easier to use. Real-life data sets are used to illustrate both the conventional and the developed method.

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“…From French economy data from Chatterjee and Hadi [23], also analyzed by Malinvaud [24], Zhang and Liu [25] and Kibria and Lukman [26], among others, the following model is analyzed:…”

Section: Economic Empirical Applicationmentioning

confidence: 99%

Detection of Near-Nulticollinearity through Centered and Noncentered Regression

2020

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This paper analyzes the diagnostic of near-multicollinearity in a multiple linear regression from auxiliary centered (with intercept) and noncentered (without intercept) regressions. From these auxiliary regressions, the centered and noncentered variance inflation factors (VIFs) are calculated. An expression is also presented that relates both of them. In addition, this paper analyzes why the VIF is not able to detect the relation between the intercept and the rest of the independent variables of an econometric model. At the same time, an analysis is also provided to determine how the auxiliary regression applied to calculate the VIF can be useful to detect this kind of multicollinearity.

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A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

Cited by 135 publications

References 32 publications

Predictive modelling of COVID-19 confirmed cases in Nigeria

Predictive modelling of COVID-19 confirmed cases in Nigeria

A New Approach of Principal Component Regression Estimator with Applications to Collinear Data

Detection of Near-Nulticollinearity through Centered and Noncentered Regression

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