Imputation for incomplete high-dimensional multivariate normal data using a common factor model.

Juwon Song; Thomas R Belin

doi:10.1002/sim.1867

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Imputation for incomplete high-dimensional multivariate normal data using a common factor model.

Juwon Song·Thomas R Belin

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Abstract

It is common in applied research to have large numbers of variables measured on a modest number of cases. Even with low rates of missingness on individual variables, such data sets can have a large number of incomplete cases. Here we present a new method for handling missing continuously scaled items in multivariate data, based on extracting common factors to reduce the number of covariance parameters to be estimated in a multivariate normal model. The technique is compared in several simulation settings to available-case analysis and to a multivariate normal model with a ridge prior. The method is also illustrated on a study with over 100 variables evaluating an emergency room intervention for adolescents who attempted suicide.

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Humans Models, Statistical Multivariate Analysis Suicide, Attempted United States Journal Article Research Support, U.S. Gov't, P.H.S.

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Juwon Song, Thomas R Belin. 2004-09-30. Imputation for incomplete high-dimensional multivariate normal data using a common factor model.. https://doi.org/10.1002/sim.1867

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