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20192026
most citedSufficient dimension reduction for regression with metric space-valued responses

1 citations · 1 across the 8 of their papers we have counts for

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6 papers · 1 filter

stat.ME2026

Robust Dual-Regularized Variable Selection under Outlier Contamination

Abdul-Nasah Soale, Adewale F. Lukman, Essoham Ali

Real data often contain unusual observations that can exert disproportionate effects on variable selection, especially in complex predictor settings. We propose a two-stage {\it sp…

stat.ME2026

Near-Optimal Nitrogen Recommendations for Precision Agriculture via Sequential Screening and Hierarchical Refinement

Sakshi Arya, Abdul-Nasah Soale, Hossein Moradi Rekabdarkolaee

Nitrogen fertilizer management plays a central role in balancing agricultural productivity and environmental sustainability, yet identifying optimal application strategies remains…

stat.ME2025

Adaptive Influence Diagnostics in High-Dimensional Regression

Abdul-Nasah Soale, Adewale Lukman

An adaptive Cook's distance (ACD) for diagnosing influential observations in high-dimensional single-index models with multicollinearity and outlier contamination is proposed. ACD…

stat.ME2024

On metric choice in dimension reduction for Fréchet regression

Abdul-Nasah Soale, Congli Ma, Siyu Chen +1

Fréchet regression is becoming a mainstay in modern data analysis for analyzing non-traditional data types belonging to general metric spaces. This novel regression method is espec…

stat.ME2023★ 1 cited

Sufficient dimension reduction for regression with metric space-valued responses

Abdul-Nasah Soale, Yuexiao Dong

Data visualization and dimension reduction for regression between a general metric space-valued response and Euclidean predictors is proposed. Current Fréchét dimension reduction m…

stat.ME2022

A selective review of sufficient dimension reduction for multivariate response regression

Yuexiao Dong, Abdul-Nasah Soale, Michael D. Power

We review sufficient dimension reduction (SDR) estimators with multivariate response in this paper. A wide range of SDR methods are characterized as inverse regression SDR estimato…