Imputation for incomplete high‐dimensional multivariate normal data using a common factor model
- 24 August 2004
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 23 (18) , 2827-2843
- https://doi.org/10.1002/sim.1867
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. Copyright © 2004 John Wiley & Sons, Ltd.Keywords
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