Robust estimation of multivariate covariance components
Related Experiment Video
Updated: Aug 19, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
1Department of Mathematics and Statistics, Arizona State University, Tempe, Arizona 85287-1804, USA. adueck@asu.edu
Abstract:
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.
Related Concept Videos
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Vector Algebra: Method of Components
In many applications, the magnitudes and directions of...
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the Guinness...
Estimating Population Standard Deviation
Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Estimation of the Physical Quantities

