dc.creatorPOLETO, Frederico Z.
dc.creatorSINGER, Julio M.
dc.creatorPAULINO, Carlos Daniel
dc.date.accessioned2012-10-20T04:44:20Z
dc.date.accessioned2018-07-04T15:46:06Z
dc.date.available2012-10-20T04:44:20Z
dc.date.available2018-07-04T15:46:06Z
dc.date.created2012-10-20T04:44:20Z
dc.date.issued2011
dc.identifierSTATISTICS AND COMPUTING, v.21, n.1, p.31-43, 2011
dc.identifier0960-3174
dc.identifierhttp://producao.usp.br/handle/BDPI/30457
dc.identifier10.1007/s11222-009-9143-x
dc.identifierhttp://dx.doi.org/10.1007/s11222-009-9143-x
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1627096
dc.description.abstractWe 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.
dc.languageeng
dc.publisherSPRINGER
dc.relationStatistics and Computing
dc.rightsCopyright SPRINGER
dc.rightsrestrictedAccess
dc.subjectCategorical data
dc.subjectMissing or incomplete data
dc.subjectMAR, MCAR and MNAR
dc.subjectIgnorable and non-ignorable mechanism
dc.subjectSelection models
dc.titleMissing data mechanisms and their implications on the analysis of categorical data
dc.typeArtículos de revistas


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