Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models

Abstract

A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The bias-corrected bootstrap estimator can be viewed as a possible alternative to the traditionally used one that is presently implemented in popular covariance structure modeling programs, and is illustrated by means of a numerical example.