The Asymptotic Effect of Substituting Estimators for Parameters in Certain Types of Statistics

In a variety of statistical problems, one is interested in the limiting distribution of statistics $\hat{T}_n = T_n(y_1, y_2, \cdots, y_n; \hat{\lambda}_n)$, where $\hat{\lambda}_n$ is an estimator of a parameter in the distribution of the $y_i$ and where the limiting distribution of $T_n = T_n(y_1, y_2, \cdots, y_n; \lambda)$ is relatively easy to find. For cases in which the limiting distribution of $T_n$ is normal with mean independent of $\lambda$, a useful method is given for finding the limiting distribution of $\hat{T}_n$. A simple application to testing normality in regression models is given.