Sufficient dimension reduction is a rapidly developing research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

Sufficient dimension reduction (SDR) methods are popular model-free tools for
preprocessing and data visualization in regression problems where the number of variables …

In this paper we propose a sufficient dimension reduction algorithm based on the difference
of inverse medians. The classic methodology based on inverse means in each slice was …

Following recent developments of dimension reduction algorithms for a multivariate time
series, we propose in this work the adaptation of sliced inverse mean difference algorithm …

Many well-known sufficient dimension reduction methods investigate the inverse conditional
moments of the predictors given the response. The required linearity condition, the number …

In this paper, we present the Cumulative Median Estimation (CUMed) algorithm for robust
sufficient dimension reduction. Compared with non-robust competitors, this algorithm …

In this work we try to address the imbalance of the number of points which naturally occurs
when slicing the response in Sufficient Dimension Reduction methods (SDR). Specifically …