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COMPARING MULTI-RIDGE AND INVERSE-RIDGE REGRESSIONS FOR DATA WITH AND WITHOUT MULTICOLLINEARITY USING VARIOUS SHRINKAGE FACTORS

Nnena Ngozi Okoro·Samuel Chukwudi Nwachukwu
Published 26 February 2025
Vol. 12, No. 3 (2024)
pp. 8-22
CC BY 4.0
  1. 1
    Nnena Ngozi Okoro
    Mathematics/Statistics Department, Ignatius Ajuru University of Education, Rumuolumeni, Port Harcourt, Rivers State, Nigeria.
    NG
  2. 2
    Samuel Chukwudi Nwachukwu
    Mathematics/Statistics Department, Ignatius Ajuru University of Education, Rumuolumeni, Port Harcourt, Rivers State, Nigeria.
    NG

The study presented Mult-, and Inverse-ridge regressions for data with or without multicollinearity for certain shrinkage factors. The study considered data of GDP of Nigeria as response, while exchange, unemployment, inflation and foreign direct investment were used as the predictors. The data were tested for outlier using Grubb’s test and the VIF, condition number, correlation and t-values were used to assess how the OLS and Ridge regressions were related with the proposed mult-and inverse-ridge regressions. The study revealed that whether or not, there is outlier or multicollinearity in a data set, the mult or inverse-ridge gives the same estimate of model parameters with the respective shrinkage factors of 1.000006 and 0.999999. These methods, overcame the barrier of testing for outlier or multicollinearity in a data set, it is advised that instead of testing, use any of the methods, Ridge, Sub-Ridge, Multi-Ridge and Inverse-Ridge methods with their respective shrinkage penalty. The OLS was not condemned, rather, it was used as the basis for judging these proposed methods.

JournalInternational Journal of Data Science and Statistics
ISSN3065-0577
Volume / IssueVol. 12, No. 3 (2024)
Pages8-22
Published26 February 2025
DOI10.5281/zenodo.14930399
Access Open Access
LicenseCC BY 4.0 — reuse with attribution
PublisherKeith Publications
Okoro, N., Nwachukwu, S. (2025). COMPARING MULTI-RIDGE AND INVERSE-RIDGE REGRESSIONS FOR DATA WITH AND WITHOUT MULTICOLLINEARITY USING VARIOUS SHRINKAGE FACTORS. International Journal of Data Science and Statistics, Vol. 12 No. 3, pp. 8-22. DOI: https://doi.org/10.5281/zenodo.14930399

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