Publications dans lesquelles il/elle collabore avec ROMÁN SALMERÓN GÓMEZ (52)

2024

  1. A review of ridge parameter selection: minimization of the mean squared error vs. mitigation of multicollinearity

    Communications in Statistics: Simulation and Computation, Vol. 53, Núm. 8, pp. 3686-3698

  2. The Raise Regression: Justification, Properties and Application

    International Statistical Review

2022

  1. Limitations in Detecting Multicollinearity due to Scaling Issues in the mcvis Package

    R Journal, Vol. 14, Núm. 4, pp. 264-279

  2. The coefficient of determination in the ridge regression

    Communications in Statistics: Simulation and Computation, Vol. 51, Núm. 1, pp. 201-219

  3. The multiColl Package Versus Other Existing Packages in R to Detect Multicollinearity

    Computational Economics, Vol. 60, Núm. 2, pp. 439-450

2021

  1. A Guide to Using the R Package “multiColl” for Detecting Multicollinearity

    Computational Economics, Vol. 57, Núm. 2, pp. 529-536

  2. Analysis of the condition number in the raise regression

    Communications in Statistics - Theory and Methods, Vol. 50, Núm. 24, pp. 6195-6210

  3. Confronting collinearity in environmental regression models: evidence from world data

    Statistical Methods and Applications, Vol. 30, Núm. 3, pp. 895-926

  4. Obtaining a threshold for the stewart index and its extension to ridge regression

    Computational Statistics, Vol. 36, Núm. 2, pp. 1011-1029

2020

  1. A Geometrical Interpretation of Collinearity: A Natural Way to Justify Ridge Regression and Its Anomalies

    International Statistical Review, Vol. 88, Núm. 3, pp. 776-792

  2. Comment on “A Note on Collinearity Diagnostics and Centering” by Velilla (2018)

    American Statistician, Vol. 74, Núm. 1, pp. 68-71

  3. Detection of near-nulticollinearity through centered and noncentered regression

    Mathematics, Vol. 8, Núm. 6

  4. Diagnosis and quantification of the non-essential collinearity

    Computational Statistics, Vol. 35, Núm. 2, pp. 647-666

  5. Raise regression: Types of raising and mean square error

    XXXIII Congreso Internacional de economía aplicada Asepelt 2019: economía azul

  6. Residualization: justification, properties and application

    Journal of Applied Statistics, Vol. 47, Núm. 11, pp. 1990-2010

  7. Stewart index and collinearity: The case of raise regression

    XXXIII Congreso Internacional de economía aplicada Asepelt 2019: economía azul

  8. The VIF and MSE in raise regression

    Mathematics, Vol. 8, Núm. 4

2019

  1. Choice of the ridge factor from the correlation matrix determinant

    Journal of Statistical Computation and Simulation, Vol. 89, Núm. 2, pp. 211-231

  2. 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)

    Natural Hazards

  3. Environmental efficiency and technological readiness: An evidence from EU-28

    Estudios de economía aplicada, Vol. 37, Núm. 1, pp. 24-34