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

Comparative simulation study demonstrates improved variable selection and robustness in thalassemia patient data, highlighting the efficiency of weighted M-estimation.

Key Points

Robust weighted least squares using optimal M-estimators preserves model stability and reduces dimensions, effectively overcoming non-normal data distributions.
Simulation analysis shows this weighting framework surpasses ordinary least squares and robust adaptive lasso across varied sample sizes and contamination rates.
Application to clinical data from thalassemia patients confirms weighted M-estimators refine variable selection, though wider validation across diverse cohorts remains needed.

Cite This Study

Selevany et al. (2024) studied this question.

synapsesocial.com/papers/68e6a9bfb6db64358762c337 https://doi.org/10.33899/iqjoss.2024.183230

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Implication

Comparative simulation study demonstrates improved variable selection and robustness in thalassemia patient data, highlighting the efficiency of weighted M-estimation.

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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