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An iterative procedure is proposed for outlier detection in linear models which is computationally simpler. We consider the deviation of each residual from its median to measure the likelihood of the corresponding data point to be an outlier. Also, the proposed work develops a reliable algorithm to estimate parameters of regression model that is unaffected by outliers. The significance of the proposed work is a novel strategy of defining direct weights without the influence function. The weight function so defined is used to estimate the parameters and to detect outliers. Our proposal is illustrated using Monte Carlo simulation.

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A Robust Regression Estimation Technique Using Direct Weights Based on Median

  • 会议论文

  • 发表日期：

    2026年

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  • Greeshmagiri,
  • T. Palanisamy

摘要

An iterative procedure is proposed for outlier detection in linear models which is computationally simpler. We consider the deviation of each residual from its median to measure the likelihood of the corresponding data point to be an outlier. Also, the proposed work develops a reliable algorithm to estimate parameters of regression model that is unaffected by outliers. The significance of the proposed work is a novel strategy of defining direct weights without the influence function. The weight function so defined is used to estimate the parameters and to detect outliers. Our proposal is illustrated using Monte Carlo simulation.