What is it about?
The paper discusses an effort to robustify maximum likelihood estimator (MLE) based regression analysis by maximizing the β-likelihood function. The tuning parameter β is selected by cross-validation. The proposed method performs better than traditional methods in both outliers and high leverage points to estimate the parameters and mean square errors. The results of relative efficiency analysis show that the proposed estimator is relatively less affected than the popular estimators, including S, MM, and fast-S for normal error distribution in case of high dimensions and outliers.
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Why is it important?
The research work also includes real data analysis results that demonstrate that the proposed method shows robust properties with respect to data contaminations, overcome the drawback of the traditional methods. Genome-wide association studies (GWAS) by the proposed method identify the vital gene influencing hypertension and iron level in the liver and spleen of mice. Furthermore, 15 and 21 significant SNPs for chalkiness degree and chalkiness percentage, respectively, were identified by GWAS based on the proposed method.
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This page is a summary of: Robustification of Linear Regression and Its Application in Genome-Wide Association Studies, Frontiers in Genetics, June 2020, Frontiers, DOI: 10.3389/fgene.2020.00549.
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