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Imputation for incomplete high‐dimensional multivariate normal data using a common factor model
Author(s) -
Song Juwon,
Belin Thomas R.
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1867
Subject(s) - multivariate statistics , missing data , multivariate normal distribution , multivariate analysis , statistics , imputation (statistics) , covariance , computer science , data mining , mathematics
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.