论文标题
偏斜正常线性混合模型的比例混合物与受试者内部依赖性
Scale mixture of skew-normal linear mixed models with within-subject serial dependence
论文作者
论文摘要
在纵向研究中，随着时间的推移会收集重复的措施，因此它们倾向于串行相关。在本文中，我们考虑了Lachos等人引入的偏斜正常/独立线性混合模型的扩展。 （2010年），其中误差项具有依赖性结构，例如降低指数相关性或顺序p的自回归相关性。当连续重复措施串行相关时，提出的模型在同时捕获偏度和重尾的影响方面提供了灵活性。对于此强大的模型，我们提出了一种有效的EM-TYPE算法，用于计算参数的最大似然估计，并且在分析上得出了观察到的信息矩阵以说明标准误差。该方法通过应用于精神分裂症数据和几项模拟研究来说明。提出的算法和方法是在新的R软件包skewlmm中实现的。
In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. In this paper we consider an extension of skew-normal/independent linear mixed models introduced by Lachos et al. (2010), where the error term has a dependence structure, such as damped exponential correlation or autoregressive correlation of order p. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously when continuous repeated measures are serially correlated. For this robust model, we present an efficient EM-type algorithm for computation of maximum likelihood estimation of parameters and the observed information matrix is derived analytically to account for standard errors. The methodology is illustrated through an application to schizophrenia data and several simulation studies. The proposed algorithm and methods are implemented in the new R package skewlmm.
