.png){kind=icon}
返回
Nonstandard conditionally specified models for nonignorable missing data
.png){kind=icon}
OA
AI
DOI：10.1073/pnas.1815563117.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive knowledge about the problem at hand, thereby offering a possibly realistic portrayal of the data, both observed and missing. We develop Tukey's representation for exponential-family models, propose a computationally tractable approach to inference in this class of models, and offer some general theoretical comments. We then illustrate the utility of this approach with an example in systems biology.
Keyword:
missing not at random
nonignorable missingness mechanism
Tukey's representation
Bayesian analysis
exponential tilting
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
P
IF:
9.1
论文数:
10.8W
被引数:
73.5W
机构
引用论文
Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology从高通量生物学中的多实验室测量估算结构化协方差矩阵OAAI
OAAI分享收藏Pattern-mixture and selection models for analysing longitudinal data with monotone missing patterns
PREAI
分享收藏Effectiveness and cost effectiveness of early and late prevention of HIV/AIDS progression with antiretrovirals or antibiotics in Southern African adults在南部非洲成年人中使用抗逆转录病毒药物或抗生素早期和晚期预防HIV/AIDS进展的有效性和成本效益.png)
AIDS Care
AIDS CareIF0
PREAI
分享收藏
分享收藏
查看更多内容