Properties of maximum likelihood estimators of variance components in the one-way classification, balanced data

Abstract

We studied properties of maximum likelihood estimators (MLEs) of the variance components obtained from balanced data of the one-way classification. Exact and asymptotic expected values and variances of these MLEs were derived under the usual normality assumptions. Numerical studies illustrate these expected values and variances, and also illustrate the probability of obtaining a negative solution to the maximum likelihood (ML) equation for the between-class variance component. Simulations were used to study the robustness of the ML estimators under non-normal distributions.

Keywords:

• variance components
• expected values
• variances
• asymptotic results
• probability of negative solution to the ML equation
• robustness