Guzmán Daniel C F, Ferreira Clécio S, Zeller Camila B
Department of Statistics, Federal University of Juiz de Fora, Juiz de Fora, Brazil.
J Appl Stat. 2020 Jul 21;48(16):3060-3085. doi: 10.1080/02664763.2020.1795814. eCollection 2021.
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，某些受试者可能没有对响应变量进行完整观测。这种对响应变量的不完整观测称为删失。删失可能由于多种原因发生，包括测量设备的局限性、实验设计以及在研究结束前感兴趣的事件未发生等。在存在删失的情况下，可以通过回归分析来探究响应变量与解释变量之间的依赖关系。在本文中，我们提议在非对称类别背景下研究删失问题，即我们基于正态分布的偏斜尺度混合提出了一个具有删失响应的线性回归模型。我们开发了一种蒙特卡罗期望最大化（MCEM）算法，用于在所提出的基于正态分布偏斜尺度混合的线性删失回归模型中对参数进行最大似然推断。本文讨论了MCEM算法，并重点关注了偏态正态、偏态学生t正态、偏斜斜线和偏态污染正态分布。为了检验所提方法的性能，我们给出了一些模拟研究并分析了一个真实数据集。
