Robust estimation of multivariate covariance components.
In many settings, such as interlaboratory testing, small area estimation in sample surveys, and heritability studies, investigators are interested in estimating covariance components for multivariate measurements. However, the presence of outliers can seriously distort estimates obtained using standard procedures such as maximum likelihood. We propose a procedure based on M-estimation for robustly estimating multivariate covariance components in the presence of outliers; the procedure applies to balanced and unbalanced data. We present an algorithm for computing the robust estimates and examine the performance of the estimator through a simulation study. The estimator is used to find covariance components and identify outliers in a study of variability of egg length and breadth measurements of American coots.
Dueck
Amylou
A
Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona 85287-1804, USA. adueck@asu.edu
Lohr
Sharon
S
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D001699:Biometry; D001717:Birds; D016013:Likelihood Functions; D012107:Research Design / Q000706:statistics & numerical data*; D015053:Zygote / Q000254:growth & development