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ORIGINAL RESEARCH article

Front. Appl. Math. Stat.
Sec. Statistics and Probability
Volume 10 - 2024 | doi: 10.3389/fams.2024.1471222

Weighted Fast Trimmed Likelihood Estimator for Mixture Regression Models

Provisionally accepted
Hassan S. Uraibi Hassan S. Uraibi *Mohammed Q. Waheed Mohammed Q. Waheed
  • University of Al-Qadisiyah, Al Diwaniyah, Iraq

The final, formatted version of the article will be published soon.

    The fast-trimmed likelihood estimate is a robust method to estimate the parameters of a mixture regression model. Unfortunately, this method is not resistant to the presence of bad leverage points, which are outliers in the direction of independent variables. The weighted, fast-trimmed likelihood estimate has been proposed in this manuscript to overcome the problem of leverage points. The proposed method employs the weights of the minimum covariance determinant with the suspected rows that probably have leverage points. Notably, real data and simulation studies have been considered to determine the efficiency of the proposed method compared with the previous methods. Accordingly, the result reveals that the weighted fast-trimmed estimate method is more robust and reliable than the fast-trimmed estimate and Expectation-Maximization (EM) methods, where the sample sizes are small.

    Keywords: Leverage point, Mixture regression, RMVN, TLE, EM, outlier

    Received: 26 Jul 2024; Accepted: 10 Sep 2024.

    Copyright: © 2024 Uraibi and Waheed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hassan S. Uraibi, University of Al-Qadisiyah, Al Diwaniyah, Iraq

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