Robust descriptive discriminant analysis for repeated measures data
Discriminant analysis (DA) procedures based on parsimonious mean and/or covariance structures have recently been proposed for repeated measures data. However, these procedures rest on the assumption of a multivariate normal distribution. This study examines repeated measures DA (RMDA) procedures based on maximum likelihood (ML) and coordinatewise trimming (CT) estimation methods and investigates bias and root mean square error (RMSE) in discriminant function coefficients (DFCs) using Monte Carlo techniques. Study parameters include population distribution, covariance structure, sample size, mean configuration, and number of repeated measurements. The results show that for ML estimation, bias in DFC estimates was usually largest when the data were normally distributed, but there was no consistent trend in RMSE. For non-normal distributions, the average bias of CT estimates for procedures that assume unstructured group means and structured covariances was at least 40% smaller than the values for corresponding procedures based on ML estimators. The average RMSE for the former procedures was at least 10% smaller than the average RMSE for the latter procedures, but only when the data were sampled from extremely skewed or heavy-tailed distributions. This finding was observed even when the covariance and mean structures of the RMDA procedure were mis-specified. The proposed robust procedures can be used to identify measurement occasions that make the largest contribution to group separation when the data are sampled from multivariate skewed or heavy-tailed distributions.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2782-2794. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.