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<p>A constrained robust regression estimation method for interval valued data is proposed involving distinct multiple linear models for mid and spread values of the data respectively. A conventional robust estimation for real valued data is made for mid model using a hybrid weight function. A novel constrained optimization is proposed to estimate the spread model. The proposed estimation technique ensures meaningful response intervals and also detects outliers. Our proposal is illustrated using Monte Carlo simulation and a real time data set. The comparative analysis reveals the efficiency of the proposed method.</p>

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Constrained Robust Regression of Interval Valued Data

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • Greeshmagiri,
  • T. Palanisamy

摘要

A constrained robust regression estimation method for interval valued data is proposed involving distinct multiple linear models for mid and spread values of the data respectively. A conventional robust estimation for real valued data is made for mid model using a hybrid weight function. A novel constrained optimization is proposed to estimate the spread model. The proposed estimation technique ensures meaningful response intervals and also detects outliers. Our proposal is illustrated using Monte Carlo simulation and a real time data set. The comparative analysis reveals the efficiency of the proposed method.