Given a sample \(X_1, X_2,\dots, X_n\) from a continuous density function \(f(x)\) and distribution function \(F(x)\), \(\hat{\eta}\) is defined by
$$\hat{\eta}=-\frac{\sum_{i=1}^n {U_iV_i}-n\bar{U}\bar{V}}{\sqrt{(\sum_{i=1}^n{U_i^2-n\bar{U^2}})(\sum_{i=1}^n{V_i^2-n\bar{V^2}})}}$$
where
$$U_i = \hat{f}(X_i), \; V_i =\hat{F}(X_i), \; \bar{U}=n^{-1}\sum_{i=1}^n U_i, \; \bar{V}=n^{-1}\sum_{i=1}^n V_i. $$