Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator by Adewale F. Lukman, Suleiman Mohammed, Olalekan Olaluwoye, Rasha A. Farghali

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Kibria–Lukman Hybrid Estimator for Handling Multicollinearity in Poisson Regression Model: Method and Application by Hleil Alrweili

“…To address multicollinearity in PRM, we propose a novel Kibria–Lukman hybrid estimator. …”
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Comparing the Linear and Quadratic Discriminant Analysis of Diabetes Disease Classification Based on Data Multicollinearity by Autcha Araveeporn

“…Our simulation study generated the independent variables by setting the coefficient correlation via multivariate normal distribution or multicollinearity, often through basic logistic regression used to construct the binary dependent variable. …”
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Spatial Modeling of Travel Demand Accounting for Multicollinearity and Different Sampling Strategies: A Stop-Level Case Study by Samuel de França Marques, Cira Souza Pitombo, J. Jaime Gómez-Hernández

“…The main contributions are as follows: by accounting for the spatial heterogeneity of the predictor dataset, the GWPCA can identify the most important factor affecting transit ridership even in bus stops with no information on boarding and alighting; the spatial modeling of stop-level ridership data using GWPCA components as explanatory variables allows visualizing the spatially varying effects from predictors on ridership, supporting the land use planning at a local level; GWPCA coupled with kriging simultaneously addresses the multicollinearity of predictor data, its spatial heterogeneity, and the spatial dependence of the stop-level ridership variable, thus enhancing the goodness-of-fit measures of the transit ridership prediction in unsampled stops; and a balanced sample on predictor data and well-spread in the geographic space might be preferred to accurately estimate missing stop-level ridership data. …”
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Application of three statistical approaches to explore effects of dietary intake of multiple persistent organic pollutants on ER-positive breast cancer risk in the French E3N cohor... by Pauline Frenoy, Ismaïl Ahmed, Chloé Marques, Xuan Ren, Gianluca Severi, Vittorio Perduca, Francesca Romana Mancini

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Optimizing rice (Oryza sativa L.) yield and lodging resistance using MGIDI and conventional selection indices by Durga Prasad Mullangie1,Kalaimagal Thiyagarajan1*,Manonmani Swaminathan2, Sritharan Natarajan2 and Senthilkumar Govindan2

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Dynamics of global economic growth: a Bayesian exploration of basic and augmented Solow models by Nguyen Thi My Diem, Nguyen Ngoc Thach

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Identification of Leverage Points in Principal Component Regression and r-k Class Estimators with AR(1) Error Structure by Tuğba Söküt

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A Comparison of Autometrics and Penalization Techniques under Various Error Distributions: Evidence from Monte Carlo Simulation by Faridoon Khan, Amena Urooj, Kalim Ullah, Badr Alnssyan, Zahra Almaspoor

“…High levels of multicollinearity adversely affect the performance of Autometrics. …”
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A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications by B. M. Golam Kibria, Adewale F. Lukman

“…This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model. …”
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A New Ridge-Type Estimator for the Gamma Regression Model by Adewale F. Lukman, Issam Dawoud, B. M. Golam Kibria, Zakariya Y. Algamal, Benedicta Aladeitan

“…However, the MLE becomes unstable in the presence of multicollinearity for both models. In this study, we propose a new estimator and suggest some biasing parameters to estimate the regression parameter for the gamma regression model when there is multicollinearity. …”
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Risk factor analysis for stunting incidence using sparse categorical principal component logistic regression by Anna Islamiyati, Muhammad Nur, Abdul Salam, Wan Zuki Azman Wan Muhamad, Dwi Auliyah

“…Therefore, we developed a sparse categorical principal component logistic regression model capable of handling data with multicollinearity. The parameters of the sparse categorical principal component logistic regression model were estimated using the maximum likelihood method and the Newton-Raphson iterative approach. …”
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Cross-project software defect prediction based on the reduction and hybridization of software metrics by Ahmed Abdu, Zhengjun Zhai, Hakim A. Abdo, Sungon Lee, Mohammed A. Al-masni, Yeong Hyeon Gu, Redhwan Algabri

“…However, these existing CPDP studies encounter two primary challenges: class overlap due to reduced feature dimensions and multicollinearity from integrating various software metrics. …”
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A Stochastic Restricted Principal Components Regression Estimator in the Linear Model by Daojiang He, Yan Wu

“…We propose a new estimator to combat the multicollinearity in the linear model when there are stochastic linear restrictions on the regression coefficients. …”
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Modified One-Parameter Liu Estimator for the Linear Regression Model by Adewale F. Lukman, B. M. Golam Kibria, Kayode Ayinde, Segun L. Jegede

“…Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. …”
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Which, renewable or non-renewable energy, is more vital for the economic growth of OECD countries? A Bayesian hierarchical analysis by Nguyen Ngoc Thach

“…Conventional statistical methods face challenges like multicollinearity when exploring the impacts of these energy sources on economic growth. …”
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A Note on the Performance of Biased Estimators with Autocorrelated Errors by Gargi Tyagi, Shalini Chandra

“…It is a well-established fact in regression analysis that multicollinearity and autocorrelated errors have adverse effects on the properties of the least squares estimator. …”
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A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model by Yao Dong, He Jiang

“…On the other hand, hard ridge penalty function is able to handle both the high multicollinearity and selection inconsistency. The real data experiments reveal the superior performances to other comparing approaches.…”
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Procurement Practices and Public Service Delivery in a Developing Local Government by Moses, Agaba, Kalu O., Emenike

“…There is absence of multicollinearity in the variables. The estimates from the multiple regression analysis suggest that procurement policy; procurement planning and sustainable procurement have positive and significant effect on public service delivery at the 5% percent significance level. …”
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Product Innovation, Price Level And Competitive Advantage: A Perception Assessment Of Beer Products by Emenike O., Kalu, Olutayo, Osunsan, Moses, Agaba

“…The data approximates normal distribution, with absence of multicollinearity. The results of the multiple regression analysis indicate that product innovation and prices have significant effect on competitive advantage among beer products and producers in Kabale Uganda. …”
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