Doubly-truncated data arise in many fields, including economics, engineering, medicine, and astronomy. This article develops likelihood-based inference methods for lifetime distributions under the log-location-scale model and the accelerated failure time model based on doubly-truncated data. These parametric models are practically useful, but the methodologies to fit these models to doubly-truncated data are missing. We develop algorithms for obtaining the maximum likelihood estimator under both models, and propose several types of interval estimation methods. Furthermore, we show that the confidence band for the cumulative distribution function has closed-form expressions. We conduct simulations to examine the accuracy of the proposed methods. We illustrate our proposed methods by real data from a field reliability study, called the Equipment-S data.

截断的数据出现在许多领域，包括经济学，工程学，医学和天文学。本文开发了基于对偶截断数据的对数定位尺度模型和加速故障时间模型下的寿命分布的基于似然性的推断方法。这些参数模型实际上是有用的，但是缺少将这些模型适合于双截断数据的方法。我们开发了用于在两种模型下获得最大似然估计的算法，并提出了几种类型的区间估计方法。此外，我们表明累积分布函数的置信带具有封闭形式的表达式。我们进行仿真以检验所提出方法的准确性。我们通过现场可靠性研究的真实数据说明了我们提出的方法，