method ensures interpretability of the resulting models even under strong multicollinearity. The algorithm proceeds in the … criterion. Simulation studies demonstrate that the proposed algorithm works well with and without strong multicollinearity and …

In a partial linear model, some non-stochastic linear restrictions are imposed under a multicollinearity setting …

multicollinearity is present. The method is able to consistently select and estimate the main effects even when strong correlations are …

) test to a new ridge regression GC test (RRGC), in the presence of multicollinearity. The investigation has been carried out …-rejects the true null hypothesis when there are relatively high (but empirically common levels of) multicollinearity, while it is …

of multicollinearity. We then employ a comprehensive simulation methodology to evaluate OLS, the Equity estimator, and a … estimator performs marginally better than Ridge. In particular, Equity does well when the degree of multicollinearity among the …

The literature on structural equation models is unclear on whether and when multicollinearity may pose problems in … theory testing (Type II errors). Two Monte Carlo simulation experiments show that multicollinearity can cause problems under … certain conditions, specifically: (1) when multicollinearity is extreme, Type II error rates are generally unacceptably high …