Diagnosis and quantification of the non-essential collinearity
| Autor: | Ainara Rodríguez-Sánchez, Román Salmerón-Gómez, Catalina García-García |
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| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Variance inflation factor Variables media_common.quotation_subject 05 social sciences Linear model Regression analysis Collinearity 01 natural sciences Term (time) 010104 statistics & probability Computational Mathematics Multicollinearity 0502 economics and business Linear regression Statistics 0101 mathematics Statistics Probability and Uncertainty 050205 econometrics media_common Mathematics |
| Zdroj: | Computational Statistics. 35:647-666 |
| ISSN: | 1613-9658 0943-4062 |
| DOI: | 10.1007/s00180-019-00922-x |
| Popis: | Marquandt and Snee (Am Stat 29(1):3–20, 1975), Marquandt (J Am Stat Assoc 75(369):87–91, 1980) and Snee and Marquardt (Am Stat 38(2):83–87, 1984) refer to non-essential multicollinearity as that caused by the relation with the independent term. Although it is clear that the solution is to center the independent variables in the regression model, it is unclear when this kind of collinearity exists. The goal of this study is to diagnose the non-essential collinearity parting from a simple linear model. The collinearity indices $$k_{j}$$, traditionally misinterpreted as variance inflation factors, are reinterpreted in this paper where they will be used to distinguish and quantify the essential and non-essential collinearity. The results can be immediately extended to the multiple linear model. The study also has some recommendations for statistical software such as SPSS, Stata, GRETL or R for improving the diagnosis of non-essential collinearity. |
| Databáze: | OpenAIRE |
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