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Multiple imputation for multivariate missingdata problems: a data analyst's perspective
 Multivariate Behavioral Research
, 1998
"... Analyses of multivariate data are frequently hampered by missing values. Until recently, the only missingdata methods available to most data analysts have been relatively ad hoc practices such as listwise deletion. Recent dramatic advances in theoretical and computational statistics, however, hav ..."
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Cited by 44 (1 self)
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Analyses of multivariate data are frequently hampered by missing values. Until recently, the only missingdata methods available to most data analysts have been relatively ad hoc practices such as listwise deletion. Recent dramatic advances in theoretical and computational statistics, however, have produced a new generation of flexible procedures with a sound statistical basis. These procedures involve multiple imputation (Rubin, 1987), a simulation technique that replaces each missing datum with a set of m>1 plausible values. The m versions of the complete data are analyzed by standard completedata methods, and the results are combined using simple rules to yield estimates, standard errors, and pvalues that formally incorporate missingdata uncertainty. New computational algorithms and software described in a recent book (Schafer, 1997) allow us to create proper multiple imputations in complex multivariate settings. This article reviews the key ideas of multiple imputation, discusses the software programs currently available, and demonstrates their use on data from
Diagnostics for Multivariate Imputations ∗
, 2007
"... We consider three sorts of diagnostics for random imputations: (a) displays of the completed data, intended to reveal unusual patterns that might suggest problems with the imputations, (b) comparisons of the distributions of observed and imputed data values, and (c) checks of the fit of observed dat ..."
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Cited by 6 (2 self)
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We consider three sorts of diagnostics for random imputations: (a) displays of the completed data, intended to reveal unusual patterns that might suggest problems with the imputations, (b) comparisons of the distributions of observed and imputed data values, and (c) checks of the fit of observed data to the model used to create the imputations. We formulate these methods in terms of sequential regression multivariate imputation [Van Buuren and Oudshoom 2000, and Raghunathan, Van Hoewyk, and Solenberger 2001], an iterative procedure in which the missing values of each variable are randomly imputed conditional on all the other variables in the completed data matrix. We also consider a recalibration procedure for sequential regression imputations. We apply these methods to the 2002 Environmental Sustainability Index (ESI), a linear aggregation of 64 environmental variables on 142 countries. 1
How to Deal with Missing Categorical Data: Test of a Simple Bayesian Method
 Organizational Research Methods
, 2003
"... Citations (this article cites 17 articles hosted on the ..."
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Cited by 2 (1 self)
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Citations (this article cites 17 articles hosted on the
Balanced Repeated Replication For Stratified Multistage Survey Data Under Imputation
"... Balanced repeated replication (BRR) is a popular method for variance estimation in surveys. The standard BRR method works by first creating a set of "balanced" pseudoreplicated data sets from the original data set. For a survey estimator `, the BRR variance estimator is the average of squared devi ..."
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Cited by 2 (0 self)
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Balanced repeated replication (BRR) is a popular method for variance estimation in surveys. The standard BRR method works by first creating a set of "balanced" pseudoreplicated data sets from the original data set. For a survey estimator `, the BRR variance estimator is the average of squared deviations ` (r) \Gamma `, where ` (r) is the same as ` but based on the data in the rth pseudoreplicated data set only. When there are a large number of imputed missing values (nonrespondents), however, treating the imputed values as observed data and applying the standard BRR variance estimation formula does not produce valid variance estimators. Intuitively, the variation due to imputation can be captured by the BRR method if every pseudoreplicated data set is imputed in exactly the same way as the original data set is imputed (assuming that the data set contains flags for nonrespondents). When a random imputation method (such as random hot deck imputation, random ratio imputation,...
Imputation of Continuous Variables Missing at Random using the Method of Simulated Scores
, 2002
"... For multivariate datasets with missing values, we present a procedure of statistical inference and state its "optimal" properties. Two main assump tions are needed: (1) data are missing at random (MAR); (2) the data generating process is a multivariate normal linear regression. Disentangling the pr ..."
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For multivariate datasets with missing values, we present a procedure of statistical inference and state its "optimal" properties. Two main assump tions are needed: (1) data are missing at random (MAR); (2) the data generating process is a multivariate normal linear regression. Disentangling the problem of convergence of the iterative estimation/imputation procedure, we show that the estimator is a "method of simulated scores" (a particular case of McFadden's method of simulated moments ); thus the estimator is equivalent to maximum likelihood if the number of replications is conveniently large, and the whole procedure can be considered an optimal parametric technique for imputation of missing data.