| A review of ridge parameter selection: minimization of the mean squared error vs. mitigation of multicollinearity |
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| Limitations in Detecting Multicollinearity due to Scaling Issues in the mcvis Package |
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| The multiColl Package Versus Other Existing Packages in R to Detect Multicollinearity |
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| Confronting collinearity in environmental regression models: evidence from world data |
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| Obtaining a threshold for the stewart index and its extension to ridge regression |
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| A Guide to Using the R Package “multiColl” for Detecting Multicollinearity |
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| A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies |
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| Comment on “A Note on Collinearity Diagnostics and Centering” by Velilla (2018) |
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| Analysis of the condition number in the raise regression |
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| Detection of Near-Nulticollinearity through Centered and Noncentered Regression |
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| The VIF and MSE in Raise Regression |
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| Diagnosis and quantification of the non-essential collinearity |
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| Comment on “An extended STIRPAT model-based methodology for evaluating the driving forces affecting carbon emissions in existing public building sector: evidence from China in 2000–2015” by Ma et al. (2017) |
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| Residualization: justification, properties and application |
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| The coefficient of determination in the ridge regression |
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| Environmental efficiency and technological readiness. An evidence from EU-28,Eficiencia medioambiental y disponibilidad o disposiciÓn tecnolÓgica. Una evidencia de la ue |
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| Choice of the ridge factor from the correlation matrix determinant |
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| Transformation of variables and the condition number in ridge estimation |
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| Variance Inflation Factor and Condition Number in multiple linear regression |
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| About the limits of raise regression to reduce condition number when three explanatory variables are involved |
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| Regresión con variables ortogonales y regresión alzada en el modelo STIRPAT |
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| A note about the corrected VIF |
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| The raise estimator estimation, inference, and properties |
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| A generalized method for valuing agricultural farms under uncertainty |
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| The Regression with Orthogonal Variables and the Raise Regression in the STIRPAT Model |
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| Regresión alzada y el número de condición: algunos problemas |
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| Standardization of Variables and Collinearity Diagnostic in Ridge Regression |
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| Collinearity diagnostic applied in ridge estimation through the variance inflation factor |
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| Treatment of collinearity through orthogonal regression: An economic application |
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| Project management under uncertainty beyond beta: The generalized bicubic distribution |
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| Collinearity: revisiting the variance inflation factor in ridge regression |
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| BAYESIAN ASYMMETRIC LOGIT MODEL FOR DETECTING RISK FACTORS IN MOTOR RATEMAKING |
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| An alternative for robust estimation in Project Management |
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| Treatment of kurtosis in financial markets |
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| A New Robust Regression Model for Proportions |
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| The raise method. An alternative procedure to estimate the parameters in presence of collinearity |
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| Modeling heavy-tailed, skewed and peaked uncertainty phenomena with bounded support |
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| The methodology adapted to the European Higher Education Area. Opinion Survey,Adaptación de la metodología al Espacio Europeo de Educación Superior. Análisis de la Opinión de los Alumnos |
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| The methodology adapted to the European Higher Education Area. Opinion Survey |
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| Proposal of a new distribution in PERT methodology |
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| The generalized biparabolic distribution |
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| A model for determining efficient portfolio cropping plans in organic farming |
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| The two-sided power distribution for the treatment of the uncertainty in PERT |
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| Iterative valuation process in the method of the two beta distributions |
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| Valoración agraria: contrastes para índices y distribuciones en el método de la dos funciones de distribución |
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