July 5, 2024Journal of excellence in social sciences.

Efficiency of OLS and Huber M estimator in case of Outliers

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Authors

SSSania ShoukatThe Women University Multan SNShahbaz NawazGovernment of Pakistan MRMuhammad Muzamil RasheedGovernment of Pakistan

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Implication

Monte Carlo simulation study reveals Huber M estimators resist x-axis outliers better than ordinary least squares in simulated data, highlighting boundaries of robust regression.

Key Points

Huber M estimation retained stability against x-axis outliers across simulated trials, whereas ordinary least squares showed vulnerability to both outlier types.
Monte Carlo simulation with 1,000 randomly selected integers from a normal distribution was conducted to evaluate regression estimator performance under outliers.
Findings highlight that robust regression with Huber M estimators avoids bias from x-axis outliers, yet sensitivity to y-axis anomalies marks clear modeling limits.

Cite This Study

Shoukat et al. (2024) studied this question.

synapsesocial.com/papers/68e615d4b6db6435875a8041 https://doi.org/10.69565/jess.v3i3.329

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Implication

Monte Carlo simulation study reveals Huber M estimators resist x-axis outliers better than ordinary least squares in simulated data, highlighting boundaries of robust regression.

Efficiency of OLS and Huber M estimator in case of Outliers

Discussion

Member takes

Implication

Key Points

Cite This Study

Efficiency of OLS and Huber M estimator in case of Outliers

Discussion

Member takes

Implication

Key Points

Cite This Study

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