  Ensemble Conditional Variance Estimation (ECVE) is a novel sufficient
dimension reduction (SDR) method in regressions with continuous response and
predictors. ECVE applies to general non-additive error regression models. It
operates under the assumption that the predictors can be replaced by a lower
dimensional projection without loss of information. It is a semiparametric
forward regression model based exhaustive sufficient dimension reduction
estimation method that is shown to be consistent under mild assumptions. It is
shown to outperform central subspace mean average variance estimation (csMAVE),
its main competitor, under several simulation settings and in a benchmark data
set analysis.
