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<p>The truncated <i>t</i> (TT) distribution has been recognized as a flexible and powerful framework for handling truncated and heavy-tailed characteristics. This paper is devoted to an in-depth investigation of its mathematical properties and extends its applicability to a finite mixture formulation, referred to as the FM-TT model henceforth. An analytical Expectation Conditional Maximization Either (ECME) algorithm is developed for parameter estimation. A key advantage of this approach is that the E-step is carried out analytically, thereby preventing the need for computationally intensive Monte Carlo integration. Furthermore, a general information matrix-based approach is exploited to assess the variability of the estimated parameters. The practical utility of the proposed methodology is demonstrated through extensive experiments on both simulated and real-world datasets. Experimental results validate the effectiveness of the FM-TT model in capturing data structure and its superiority over existing approaches by achieving better model fits and improved classification accuracy.</p>

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Robust mixture modeling using the truncated t distribution: recent advances and new results

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月4日

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  • Wan-Lun Wang,
  • Si-Hui Lee,
  • Tsung-I Lin

摘要

The truncated t (TT) distribution has been recognized as a flexible and powerful framework for handling truncated and heavy-tailed characteristics. This paper is devoted to an in-depth investigation of its mathematical properties and extends its applicability to a finite mixture formulation, referred to as the FM-TT model henceforth. An analytical Expectation Conditional Maximization Either (ECME) algorithm is developed for parameter estimation. A key advantage of this approach is that the E-step is carried out analytically, thereby preventing the need for computationally intensive Monte Carlo integration. Furthermore, a general information matrix-based approach is exploited to assess the variability of the estimated parameters. The practical utility of the proposed methodology is demonstrated through extensive experiments on both simulated and real-world datasets. Experimental results validate the effectiveness of the FM-TT model in capturing data structure and its superiority over existing approaches by achieving better model fits and improved classification accuracy.