New robust estimator for handling outliers and multicollinearity in gamma regression model with application to breast cancer data [PDF]
Scientific ReportsThe gamma regression model (GRM) is commonly used to analyze continuous data that are positively skewed. However, the GRM is sensitive to multicollinearity and outliers. These two problems often occur in regression analysis.
Arwa M. Alshangiti +7 more
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Special ridge-type estimator: Simulation and application to chemical data
AIP AdvancesThis study delves into regularization techniques, such as ridge regression, Liu estimator, and Kibria–Lukman estimator, as alternatives to the maximum likelihood method for addressing multicollinearity in beta regression models.
Rasha A. Farghali +4 more
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Modified Ridge Logistic Estimator Based on Singular Value Decomposition [PDF]
The Egyptian Statistical Journal, 2023 This paper aims to introduce a modification of the ridge estimator based on the singular value decomposition (SVD) technique of the design matrix (X ) to combat multicollinearity in the binary logistic model.
Monira Hussein, Mostafa Abd el-Rahman
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Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation [PDF]
Surveys in Mathematics and its Applications, 2009 Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR).
Sharad Damodar Gore, Feras Sh. M. Batah
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Polynomial ridge flowfield estimation [PDF]
Physics of Fluids, 2021 Computational fluid dynamics plays a key role in the design process across many industries. Recently, there has been increasing interest in data-driven methods in order to exploit the large volume of data generated by such computations. This paper introduces the idea of using spatially correlated polynomial ridge functions for rapid flowfield ...
A. Scillitoe +3 more
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Estimation methods of logistic regression in context of multicollinearity (Comparative study) [PDF]
Maǧallaẗ Al-Buḥūṯ Al-Tiǧāriyyaẗ, 2023 The binary logistic regression (BLR) model is used as an alternative to the commonly used linear regression model when the response variable is binary.
Hassan Mohamed Ali +2 more
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Modified Jackknifed Ridge Estimator in Bell Regression Model: Theory, Simulation and Applications
Iraqi Journal for Computer Science and Mathematics, 2023 Regression models explore the relationship between the response variable and one or more explanatory variables. It becomes practically challenging in real-life applications to model this relationship when the explanatory variables are linearly dependent.
Zakariya Algamal +3 more
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Modified jackknife ridge estimator for the Conway-Maxwell-Poisson model
Scientific African, 2023 Recently, research papers have shown a strong interest in modeling count data. The over-dispersion or under-dispersion are frequently seen in the count data.
Zakariya Yahya Algamal +3 more
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Ridge regression and its applications in genetic studies.
PLoS ONE, 2021 With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling.
M Arashi +3 more
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Generalized ridge estimator shrinkage estimation based on particle swarm optimization algorithm [PDF]
المجلة العراقية للعلوم الاحصائية, 2020 It is well-known that in the presence of multicollinearity, the ridge estimator is an alternative to the ordinary least square (OLS) estimator. Generalized ridge estimator (GRE) is an generalization of the ridge estimator.
Qamar Abdul kareem, Zakariya Algamal
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