Nonrandom Missingness in Categorical Data: Strengt hs and Limitations
Nonrandom Missingness in Categorical Data: Strengt hs and Limitations
复制标题
分类数据中的非随机缺失：优点和局限性
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
M. Kenward
中科院分区：
文献类型：
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作者：
G. Molenberghs;E. J. T. Goetchebeur;S. Lipsitz;M. Kenward
摘要 最近，不完整数据的分析取得了实质性进展。现在可用于非随机缺失的建模工具，并且这些方法正在进入广泛的统计界。围绕此类模型的计算和解释问题不太为人所知。本文阐述了分类数据设置中的其中几个问题。有人认为，上下文信息的使用可以帮助建模者区分纯粹在统计基础上无法区分的模型。
Abstract There have recently been substantial developments in the analysis of incomplete data. Modeling tools are now available for nonrandom missingness and these methods are finding their way into the broad statistical community. The computational and interpretational issues that surround such models are less well known. This article provides an exposition of several of these issues in a categorical data setting. It is argued that the use of contextual information can aid the modeler in discriminating among models that are indistinguishable purely on statistical grounds.
产前血铅水平和儿童学习困难：非随机缺失分类数据的分析。
影响因子：
2
作者：
Conaway,MR;Waternaux,C;Allred,E;Bellinger,D;Leviton,A
通讯作者：
Leviton,A