An alternative approximation to the variance of transformation score

Abstract

An alternative approximation to the variance of transformation score is given, based on an asymptotic expansion of the transformation estimator. It is then compared with the variance approximation given by Lawrance (1987) in terms of standardized scores. Simulations show that the two standardized scores behave very similarly when model error standard deviation is small. However, when error standard deviation is not small, the new standardized score outperforms that of Lawrance, especially in the structured models.