NON-ITERATIVE ROBUST ESTIMATORS OF VARIANCE COMPONENTS IN WITHIN-SUBJECT DESIGNS

• Published in: Statistics in medicine Vol. 16; no. 13; pp. 1465 - 1479
• Main Author: MEHROTRA, DEVAN V.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 15.07.1997; Wiley
• Subjects: Analysis of Variance; Biological and medical sciences; Clinical Trials as Topic - statistics & numerical data; Computerized, statistical medical data processing and models in biomedicine; Humans; Likelihood Functions; Mathematical Computing; Medical sciences; Medical statistics; Models, Statistical; Monte Carlo Method; Reference Values; Human; Monte Carlo method; Data analysis; Clotting time; Estimator; Samplings; Blood; Variance; Statistical method; Outlier; Unbiased estimation; Robustness; Maximum likelihood; Comparative study
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/(SICI)1097-0258(19970715)16:13<1465::AID-SIM583>3.0.CO;2-W

Classic estimators of variance components break down in the presence of outliers and perform less efficiently under non‐normality. In this article I present simple non‐iterative estimators of variance components that are resistant to outliers and robust to systematic departures from normality, such as heavy tailedness of the distribution of responses. The proposed estimators are based on a robust extension of Hocking's AVE approach and are thus called RAVE estimators. I present results from a Monte Carlo comparison of RAVE versus classic estimation methods including maximum likelihood (ML), restricted maximum likelihood (REML) and minimum variance quadratic unbiased estimation (MIVQUE). Under simulated deviations from normality, RAVE estimators are associated with smaller mean squared errors than all the comparators, and in the normal case they exhibit a minimal loss in relative efficiency. A numerical example illustrates the proposed methodology. © 1997 John Wiley & Sons, Ltd.