TY  - JOUR
T1  - ADAPTIVE BOOSTED ESTIMATION FOR SINGLE-INDEX QUANTILE REGRESSION
AU  - Alshaybawee, Taha
AU  - Alhusseini, Fadel Hamid Hadi
AU  - Mzedawee, Asaad Naser Hussein
PY  - 2025
DA  - October
Y2  - 2025
JF  - TWMS Journal of Applied and Engineering Mathematics
JO  - JAEM
PB  - Işık University Press
WT  - DergiPark
SN  - 2146-1147
SP  - 2567
EP  - 2583
VL  - 15
IS  - 10
LA  - en
AB  - We propose a novel boosted estimation method for single-index quantile regression (SIQR) that combines the robustness of quantile regression with the flexibility of gradient boosting. By modeling the conditional quantile through a single linear index and a nonlinear link function, our method achieves effective dimension reduction while capturing complex relationships in the data. The procedure iteratively updates the index direction and fits base learners such as splines or regression trees to the pseudoresiduals from the quantile loss. This approach avoids multivariate smoothing, handles non-Gaussian errors, and adapts well to nonlinear structures. We establish theoretical guarantees, including consistency and optimal convergence rates under standard conditions. Extensive simulation studies and a real-data application demonstrate that the proposed method outperforms existing SIQR approaches in terms of accuracy and robustness.
KW  - Quantile regression
KW  - Single-index model
KW  - Gradient boosting
KW  - semi-parametric
quantile regression
KW  - Single-index quantile regression
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UR  - https://dergipark.org.tr/en/pub/twmsjaem/issue//1795347
L1  - https://dergipark.org.tr/en/download/article-file/5293244
ER  -