Random and Fixed Effects Selection for Weighted Ridge

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Lulah Alnaji


Using penalized profiled log-likelihood and penalized limited profiled log-likelihood, respectively, together with the weighted ridge penalized term, we offer a method in this study for choosing the fixed and random effects in linear mixed models. Then, we use the penalized restricted profiled log-likelihood to perform in the random effects depending on the chosen tuning parameter. Second, we use the penalized profiled log-likelihood to choose the fixed effect parameters. There is no closed-form solution for the choice of the fixed and random effects, hence the Newton-Raphson technique is employed to iteratively estimate the parameters. We use a simulation study to show how well the suggested strategy works. Lastly, we use two separate datasets to use the methods to further evaluate the newly proposed model.

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