Programming Correlation Criteria with free CAS Software

Halkos, George; Tsilika, Kyriaki

doi:10.1007/s10614-016-9604-1

Cited by 8 publications

(6 citation statements)

References 11 publications

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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%

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

Gómez

García

Sanchez³

et al. 2023

The R Journal

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Transformation of the observed data is a very common practice when a troubling degree of near multicollinearity is detected in a linear regression model. However, it is important to take into account that these transformations may affect the detection of this problem, so they should not be performed systematically. In this paper we analyze the transformation of the data when applying the R package mcvis, showing that it only detects essential near multicollinearity when the studentise transformation is performed.

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Section: Introductionmentioning

confidence: 59%

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

Gómez

García

Sanchez³

et al. 2023

The R Journal

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“…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%

Causality Distance Measures for Multivariate Time Series with Applications

et al. 2021

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In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.

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“…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%

Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote

2021

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When it comes to variable interpretation, multicollinearity is among the biggest issues that must be surmounted, especially in this new era of Big Data Analytics. Since even moderate size multicollinearity can prevent proper interpretation, special diagnostics must be recommended and implemented for identification purposes. Nonetheless, in the areas of econometrics and statistics, among other fields, these diagnostics are controversial concerning their “successfulness”. It has been remarked that they frequently fail to do proper model assessment due to information complexity, resulting in model misspecification. This work proposes and investigates a robust and easily interpretable methodology, termed Elastic Information Criterion, capable of capturing multicollinearity rather accurately and effectively and thus providing a proper model assessment. The performance is investigated via simulated and real data.

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Programming Correlation Criteria with free CAS Software

Cited by 8 publications

References 11 publications

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

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

Causality Distance Measures for Multivariate Time Series with Applications

Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote

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