Missing data mechanisms and their implications on the analysis of categorical data

被引:0
|
作者
Frederico Z. Poleto
Julio M. Singer
Carlos Daniel Paulino
机构
[1] Universidade de São Paulo,Departamento de Estatística, Instituto de Matemática e Estatística
[2] Universidade Técnica de Lisboa (and CEAUL-FCUL),Departamento de Matemática, Instituto Superior Técnico
来源
Statistics and Computing | 2011年 / 21卷
关键词
Categorical data; Missing or incomplete data; MAR, MCAR and MNAR; Ignorable and non-ignorable mechanism; Selection models;
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学科分类号
摘要
We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to those where the units with missing data are disregarded. We confirm that although, in general, analyses under the correct missing at random and missing completely at random models are more efficient even for small sample sizes, there are exceptions where they may not improve the results obtained by ignoring the partially classified data. We show that under the missing not at random (MNAR) model, estimates on the boundary of the parameter space as well as lack of identifiability of the parameters of saturated models may be associated with undesirable asymptotic properties of maximum likelihood estimators and likelihood ratio tests; even in standard cases the bias of the estimators may be low only for very large samples. We also show that the probability of a boundary solution obtained under the correct MNAR model may be large even for large samples and that, consequently, we may not always conclude that a MNAR model is misspecified because the estimate is on the boundary of the parameter space.
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页码:31 / 43
页数:12
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