Semiparametric Estimation of Non-ignorable Missingness With Refreshment Sample

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Jianfei Zheng, Jing Wang, L. Xue, A. Qu
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引用次数: 0
点心样本不可忽略缺失的半参数估计
数据缺失通常出现在纵向数据分析中,由于信息缺失,在提供无偏估计和统计推断方面提出了方法学上的挑战。正确识别和适当地将缺失的机制纳入估计和推理过程是至关重要的。传统方法,如全案例分析和imputation方法,都是针对MCAR和mar的不可验证假设下的缺失数据进行处理的。我们重点研究了在不可忽略的缺失假设下,利用两波面板数据的更新样本识别和估计缺失参数。具体来说,我们提出了一种全似然方法,当参数模型被指定为两波数据的联合分布时。在无法确定联合分布的情况下,提出了一种半参数方法,利用额外的茶点样本获得的边际密度估计来估计磨损参数。给出了半参数估计量的渐近性质,并用仿真说明了其数值性能。提出了基于自举的推理方法,并通过仿真对其进行了评估。根据荷兰移动小组的研究提供了一个实际数据应用。中国统计:新录用论文(接受作者版本,需英文编辑)
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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