Robust descriptive discriminant analysis for repeated measures data

Tolulope T. Sajobi, Lisa M. Lix, Bolanle M. Dansu, William Laverty and Longhai Li

Computational Statistics & Data Analysis, 2012, vol. 56, issue 9, 2782-2794

Abstract: 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.

Keywords: Bias; Covariance structure; Discriminant function coefficients; Repeated measurements; Root mean square error (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947312001119
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:9:p:2782-2794

DOI: 10.1016/j.csda.2012.02.029

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().