# Ensemble Conditional Variance Estimator for Sufficient Dimension Reduction

arXiv: https://arxiv.org/abs/2102.13435 · HTML: https://mlsanity.com/paper/2102.13435/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2021.

## Built on by (most cited first, 2 of 2)

- [Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction](https://mlsanity.com/paper/2412.08961/) (2024)
- [Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey](https://mlsanity.com/paper/2110.09620/) (2021)

## Abstract (arXiv)

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.