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Multiple imputation and posterior simulation for multivariate missing data in longitudinal studies (pp
 Proceedings of the American Statistical Association Biometrics Section
, 1995
"... SUMMARY. This paper outlines a multiple imputation method for handling missing data in designed longitudinal studies. A random coefficients model is developed to accommodate incomplete multivariate continuous longitudinal data. Multivariate repeated measures are jointly modeled; specifically, an i ..."
Abstract

Cited by 7 (1 self)
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SUMMARY. This paper outlines a multiple imputation method for handling missing data in designed longitudinal studies. A random coefficients model is developed to accommodate incomplete multivariate continuous longitudinal data. Multivariate repeated measures are jointly modeled; specifically, an i.i.d. normal model is assumed for timeindependent variables and a hierarchical random coefficients model is assumed for timedependent variables in a regression model conditional on the timeindependent variables and time, with heterogeneous error variances across variables and time points. Gibbs sampling is used to draw model parameters and for imputations of missing observations. An application to data from a study of startle reactions illustrates the model. A simulation study compares the multiple imputation procedure to the weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90, 106121) that can be used to address similar data structures. KEY WORDS: Gibbs sampling; Missing data; Multiple imputation; Multivariate longitudinal data 1. Background In designed longitudinal studies, missing data often occur because subjects miss visits during the study, because some variables may not be measured at particular visits, or because
Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data
 in Questionnaire Data”. the Annual American Educational Research Association Meeting, SIG Educational Statisticians
, 1998
"... this paper we study a new informationtheoretically justified approach to missing data estimation for multivariate categorical data. The approach discussed is a modelbased imputation procedure relative to a model class (i.e., a functional form for the probability distribution of the complete data m ..."
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this paper we study a new informationtheoretically justified approach to missing data estimation for multivariate categorical data. The approach discussed is a modelbased imputation procedure relative to a model class (i.e., a functional form for the probability distribution of the complete data matrix), which in our case is the set of multinomial models with some independence assumptions. Based on the given model class assumption an informationtheoretic criterion can be derived to select between the different complete data matrices. Intuitively this general criterion, called stochastic complexity, represents the shortest code length needed for coding the complete data matrix relative to the model class chosen. Using this informationtheoretic criteria, the missing data problem is reduced to a search problem, i.e., finding the data completion with minimal stochastic complexity. In the experimental part of the paper we present empirical results of the approach using two real data sets, and compare these results to those achived by commonly used techniques such as case deletion and imputating sample averages. Introduction