May 11, 2024IRAQI JOURNAL OF STATISTICAL SCIENCESOpen Access

Robust Weighted Least Squares Method using different schemes of M-estimators (RWLSM), A comparative Study

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Authors

FSFatima M. SelevanyUniversity of Mosul BABashar Al-TalibUniversity of Mosul

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Implication

Simulation study demonstrates robust weighted least squares preserves model efficiency under outlier contamination in thalassemia data, highlighting improved variable selection.

Key Points

Robust weighted least squares successfully limits the impact of outliers during model selection, achieving dimension reduction and enhanced efficiency in contaminated data.
Simulation across varied sample dimensions and contamination rates assessed variable retention against ordinary least squares and robust adaptive lasso frameworks.
Validation on clinical records from thalassemia patients confirms reliable regression equation selection, highlighting strong utility when outliers violate normality assumptions.

Cite This Study

Selevany et al. (2024) studied this question.

synapsesocial.com/papers/68e6a9bfb6db64358762c33b https://doi.org/10.33899/iqjoss.2024.0183230

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Implication

Simulation study demonstrates robust weighted least squares preserves model efficiency under outlier contamination in thalassemia data, highlighting improved variable selection.

Robust Weighted Least Squares Method using different schemes of M-estimators (RWLSM), A comparative Study

Discussion

Member takes

Implication

Key Points

Cite This Study

Robust Weighted Least Squares Method using different schemes of M-estimators (RWLSM), A comparative Study

Discussion

Member takes

Implication

Key Points

Cite This Study

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