Robust mean and covariance structure analysis

Abstract

Covariance structure analysis is used to evaluate hypothesized influences among unmeasured latent and observed variables. As implemented, it is not robust to outliers and bad data. Several robust methods in model fitting and testing are proposed. These include direct estimation of M‐estimators of structured parameters and a two‐stage procedure based on robust M‐ and S‐estimators of population covariances. The large sample properties of these estimators are obtained. The equivalence between a direct M‐estimator and a two‐stage estimator based on an M‐estimator of population covariance is established when sampling from an elliptical distribution. Two test statistics are presented in judging the adequacy of a hypothesized model; both are asymptotically distribution free if using distribution free weight matrices. So these test statistics possess both finite sample and large sample robustness. The two‐stage procedures can be easily adapted into standard software packages by modifying existing asymptotically distribution free procedures. To demonstrate the two‐stage procedure, S‐estimator and M‐estimators under different weight functions are calculated for some real data sets.