2016
DOI: 10.1007/s10614-016-9604-1
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Programming Correlation Criteria with free CAS Software

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Cited by 8 publications
(6 citation statements)
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“…C. García, Salmerón, and García (2019) show that values for the coefficient of simple correlation between the independent variables higher than √ 0.9 and determinant lower than 0.1013 + 0.00008626 • n − 0.01384 • p indicate a troubling degree of multicollinearity (see Román Salmerón, García, and García (2021a) or Román Salmerón, García, and García (2021b) for more details). The first value differs strongly from the threshold normally proposed equal to 0.7 to indicate a problem of near collinearity (see, for example, Halkos and Tsilika (2018)). Also useful to use the coefficient of variation (CV), values less than 0.1002506 indicate the existence of troubling multicollinearity (see Román Salmerón, Rodríguez, and García (2020) for more details).…”
Section: Introductionmentioning
confidence: 59%
“…C. García, Salmerón, and García (2019) show that values for the coefficient of simple correlation between the independent variables higher than √ 0.9 and determinant lower than 0.1013 + 0.00008626 • n − 0.01384 • p indicate a troubling degree of multicollinearity (see Román Salmerón, García, and García (2021a) or Román Salmerón, García, and García (2021b) for more details). The first value differs strongly from the threshold normally proposed equal to 0.7 to indicate a problem of near collinearity (see, for example, Halkos and Tsilika (2018)). Also useful to use the coefficient of variation (CV), values less than 0.1002506 indicate the existence of troubling multicollinearity (see Román Salmerón, Rodríguez, and García (2020) for more details).…”
Section: Introductionmentioning
confidence: 59%
“…The percentage of missing values was approximately 10%. The data analysis was conducted with the R free software, and figures were built via Tableau for Teaching and Rawgraphics [32].…”
Section: V1mentioning
confidence: 99%
“…This occurs as a consequence of coefficient penalization absence and can be resolved, to some extent, by regularization methods discussed in the next subsection. Finally, it should be noted that for specialized business and econometric computations for detecting and evaluating collinearity based on methods such as the ones presented above, one may refer to Xcas, a free programming algebra system (Halkos and Tsilika (2018)).…”
Section: Review Of Multicollinearity Measuresmentioning
confidence: 99%
“…Several partially robust criteria and indices for multicollinearity have been proposed over the years, which are based either on the coefficient of determination and similar measures or in the eigenvalue-eigenvector analysis. Theil's indicator (Theil (1971)), Klein's rule (Klein (1962)), Tolerance Limit (TOL), and Variance Inflation Factor (VIF) (Gujarati and Porter (2008)) fall into the first category while the Farrar-Glauber test Farrar and Glauber (1967), the sum of reciprocal eigenvalues, Red indicator (Kovács et al (2005)), Condition Index (Belsley (1991); Hair et al (2010)) and eigensystem analysis are some of the most frequently used measures that fall into the second (for a thorough analysis see Halkos and Tsilika (2018); Imdadullah et al (2016)). All these measures commonly use some sort of rule of thumb to rule about the existence of multicollinearity.…”
Section: Introductionmentioning
confidence: 99%