@article{ref1,
title="Imputation for incomplete high-dimensional multivariate normal data using a common factor model",
journal="Statistics in Medicine",
year="2004",
author="Song, Juwon and Belin, Thomas R.",
volume="23",
number="18",
pages="2827-2843",
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.<p /><p>Language: en</p>",
language="en",
issn="0277-6715",
doi="10.1002/sim.1867",
url="http://dx.doi.org/10.1002/sim.1867"
}