Generalized regression estimators of a finite population total using the Box-Cox technique

A new generalized regression estimator of a finite population total based on the Box-Cox transformation technique and its variance estimator are proposed under a general unequal probability sampling design. By being design consistent, the proposed estimator maintains the robustness property of the GREG estimator even if the underlying model fails. Furthermore, the Box-Cox technique automatically finds a reasonable transformation for the dependent variable using the data. The robustness and efficiency of the new estimator are evaluated analytically and via Monte Carlo simulation studies.