A Guide to Using the R Package “multiColl” for Detecting Multicollinearity
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)
Link
http://link.springer.com/content/pdf/10.1007/s10614-019-09967-y.pdf
Reference18 articles.
1. Farrar, D. E., & Glauber, R. R. (1967). Multicollinearity in regression analysis: The problem revisited. The Review of Economic and Statistics, pp. 92–107.
2. Field, A. (2019). Discovering statistics using SPSS for Windows (3rd ed.). Los Angeles: Sage Publications.
3. García, C., Salmerón, R., García, C., & García, J. (2019). Residualization: justification, properties and application. Journal of Applied Statistics (in review)
4. García, C., Salmerín, R., & Garcóa, C. (2018). A choice of the ridge factor from the correlation matrix determinant. Journal of Statistical Computation and Simulation, 2(89), 211–231. https://doi.org/10.1080/00949655.2018.1543423.
5. Gunst, R. F., & Mason, R. L. (1977). Advantages of examining multicollinearities in regression analysis. Biometrics, pp. 249–260.
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