A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset.

统计分析中一个特殊的困难来源是某些受试者可能无法完整观察响应变量。这种对响应变量的不完整观察称为审查。审查可能由于多种原因而发生，包括测量设​​备的限制、实验的设计以及在研究结束之前没有发生感兴趣的事件。在存在删失的情况下，可以通过回归分析来探索响应变量对解释变量的依赖性。在本文中，我们建议在非对称类的背景下检查审查问题，即，我们提出了一个基于正态分布的倾斜比例混合的审查响应的线性回归模型。我们开发了一种 Monte Carlo EM (MCEM) 算法，以对所提出的线性删失回归模型中的参数进行最大似然推断，该模型具有正态分布的倾斜尺度混合。已经讨论了 MCEM 算法，重点是偏斜正态分布、偏斜学生 t 正态分布、斜斜线和偏斜污染正态分布。为了检查所提出方法的性能，我们提出了一些模拟研究并分析了一个真实的数据集。