Inference for longitudinal data with nonignorable nonmonotone missing responses.
Inference for longitudinal data with nonignorable nonmonotone missing responses.
复制标题
具有不可忽略的非单调缺失响应的纵向数据的推断。
DOI：
10.1016/j.csda.2013.10.027
复制
发表时间：
2014
影响因子：
1.8
通讯作者：
Xiao,Wenzhong
中科院分区：
文献类型：
--
作者：
Sinha,SanjoyK;Kaushal,Amit;Xiao,Wenzhong
对于具有不可重复和非单调缺失响应的纵向数据的分析，全似然方法通常需要密集的计算，特别是当有许多随访时间时。作者提出并探讨了一种基于重要抽样的蒙特卡罗方法，用于逼近极大似然估计。有限样本估计的性质进行了研究，使用模拟。还提供了所提出的方法的应用程序，使用纵向数据从创伤患者的蛋白质组学实验获得的肽强度。
For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.