A New Measure of Multicollinearity in Linear Regression Models

By P. Kovács, Tibor Petres, László Tóth. 2005

65 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9ab73c007a8bd94b9c2fc1778e1e0ed0c9d848c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Catalina Beatriz García García
2. M. M. L. López Martín
3. José Castro Pinto
4. José Dias Curto

In Statistics and Probability

1. Muhammad Aslam
2. Muhammad Imdad Ullah
3. Saima Altaf
4. Munir Ahmed
5. Catalina Beatriz García García
6. José García Pérez
7. Román Salmerón Gómez
8. M. M. L. López Martín

Citations in the corpus

• Some New Diagnostics of Multicollinearity in Linear Regression Model (2019, methodology): “1980), Leamer’s method (Greene 2002), Klien’s rule (Klein 1962), three tests proposed by Farrar and Glauber (1967), Red indicator (Kovács et al. 2005) and Theil’s measure (Theil 1971).”
• The corrected VIF (CVIF) (2011, methodology): “[9] apply the redundancy number of Gleason and Staelin [4] (see also [6]), to the regression model and name it by Red indicator.”
• Collinearity diagnostic applied in ridge estimation through the variance inflation factor (2016, methodology): “[2,3], the variance decomposition proportions to analyze the correlations between different vectors and theirs angles [29,34], the red indicator [20], the corrected VIF [5], or the use of biplot method in the visualization diagnostic of multicollinearity problems called collinearity biplot [10].”