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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Factor Investment or Feature Selection Analysis? by Jifang Mai, Shaohua Zhang, Haiqing Zhao, Lijun Pan

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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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Prediction of time-dependent bearing capacity of concrete pile in cohesive soil using optimized relevance vector machine and long short-term memory models by Jitendra Khatti, Mohammadreza Khanmohammadi, Yewuhalashet Fissha

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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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Modified Ridge Regression With Cook’s Distance for Semiparametric Regression Models by Najeeb Mahmood Khan, Muhammad Aman Ullah, Javaria Ahmad Khan, Salman Raza

“…Multicollinearity and influential cases in semiparametric regression models lead to biased and unreliable estimates distorting leverage and residual patterns. …”
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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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Value of permanent crops in the gross value added in agriculture by Grujić-Vučkovski Biljana Lj., Vukićević Ksenija R., Stevanović Dragana D.

“…An analysis of the presence of multicollinearity in the independent variables was also conducted, and the results showed that there was a weak multicollinearity originating from the value of viticulture production.…”
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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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Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm by Abdelmounaim Kerkri, Jelloul Allal, Zoubir Zarrouk

“…Partial least squares regression (PLS regression) is used as an alternative for ordinary least squares regression in the presence of multicollinearity. This occurrence is common in chemical engineering problems. …”
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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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