The Teissier distribution, originally proposed by Teissier [31], was designed to model mortality due to aging in domestic animals. More recently, Krishna et al. [19] introduced the Unit Teissier (UT) distribution on the interval (0, 1) through the transformation $X=e^{-Y}$, where $Y$ follows the Teissier distribution. In their work, the authors derived several fundamental properties of the UT distribution and investigated parameter estimation using maximum likelihood, least squares, weighted least squares and Bayesian methods. Building upon this work, the present paper develops additional theoretical and inferential results for the UT distribution. In particular, closed-form expressions for single moments of order statistics and L-moments are obtained, and characterization results based on truncated moments are established. Furthermore, several alternative parameter estimation methods are considered, including maximum product of spacings, Cramér-von Mises, Anderson-Darling, right-tail Anderson-Darling, percentile and L-moment estimation, while the estimation methods previously studied by Krishna et al. [19] are also included for comparison. Extensive simulation studies under various parameter settings and sample sizes are conducted to assess and compare the performance of the estimators. Finally, the flexibility and practical utility of the UT distribution are demonstrated using a real dataset.

翻译：Teissier分布最初由Teissier [31]提出，旨在模拟家畜因衰老导致的死亡率。最近，Krishna等人[19]通过变换$X=e^{-Y}$（其中$Y$服从Teissier分布）引入了定义在区间(0, 1)上的单位Teissier（UT）分布。作者在其工作中推导了UT分布的若干基本性质，并研究了基于极大似然、最小二乘、加权最小二乘及贝叶斯方法的参数估计。本文在此工作基础上，进一步拓展了UT分布的理论与推断结果。具体而言，我们获得了次序统计量单阶矩与L-矩的闭式表达式，并建立了基于截断矩的分布特征刻画结果。此外，本文考虑了多种替代参数估计方法，包括最大间距积估计、Cramér-von Mises估计、Anderson-Darling估计、右尾Anderson-Darling估计、百分位数估计及L-矩估计，同时将Krishna等人[19]先前研究的估计方法也纳入比较范围。我们在多种参数设置与样本量条件下进行了广泛的模拟研究，以评估和比较各估计量的性能。最后，通过实际数据集展示了UT分布的灵活性与实用价值。