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Abstract <p>In this paper, we study a robust estimation method for a mixed nonlinear model. In this model, we consider that the distribution of the observations belongs to the elliptical family. For the parametric inference, we propose an estimator based on minimum density power divergence (MDPD). Under some regularity conditions, we establish the main properties of this estimator; that is, consistency and asymptotic normality. We illustrate the robustness of the MDPDE compared to the maximum likelihood using some Monte Carlo simulations. Finally, we provide the results of an application on real data.</p>

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Minimum Density Power Divergence Estimator (MDPDE) for Nonlinear Mixed Elliptical Models

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Paliguiwendé Dieudonné Ibrango,
  • Aliou Diop,
  • Mamadou Lamine Diop,
  • Diakarya Barro

摘要

Abstract

In this paper, we study a robust estimation method for a mixed nonlinear model. In this model, we consider that the distribution of the observations belongs to the elliptical family. For the parametric inference, we propose an estimator based on minimum density power divergence (MDPD). Under some regularity conditions, we establish the main properties of this estimator; that is, consistency and asymptotic normality. We illustrate the robustness of the MDPDE compared to the maximum likelihood using some Monte Carlo simulations. Finally, we provide the results of an application on real data.