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ResearchFormerly WizWand

Quantile Regression on Scenario 1 n=1000

0.3316MSE (τ=0.05)Baseline
0.3179320.4101910.502450.594709May 7, 2026
Updated 6mo ago

Evaluation results

MethodLinks
BaselineModel=Model A2026.050.33160.05750.03240.03760.3508
ConquerNetModel=Model A, Kernel=Epanechnikov2026.050.33560.06290.02250.02590.3973
BaselineModel=Model B2026.050.34570.07160.03730.04240.3231
ConquerNetModel=Model B, Kernel=Gaussian2026.050.35250.07970.03060.03080.5173
ConquerNetModel=Model B, Kernel=Uniform2026.050.35960.07950.03220.03520.4551
ConquerNetModel=Model B, Kernel=Epanechnikov2026.050.3680.08710.03480.0304—
ConquerNetModel=Model A, Kernel=Uniform2026.050.37190.05810.02180.02340.4008
ConquerNetModel=Model A, Kernel=Gaussian2026.050.37870.0620.02170.02620.4817
BaselineModel=Model A2026.050.3820.04020.02780.03740.3784
BaselineModel=Model B2026.050.38420.05270.03190.04750.4222
ConquerNetModel=Model B, Smoothing Kernel=Gaussian2026.050.41580.06650.03830.06330.5202
ConquerNetModel=Model B, Smoothing Kernel=Epanechnikov2026.050.42750.05820.03830.06260.605
ConquerNetModel=Model A, Smoothing Kernel=Gaussian2026.050.43540.03830.02240.03820.5035
ConquerNetModel=Model A, Smoothing Kernel=Uniform2026.050.44480.03850.02240.03280.3721
ConquerNetModel=Model B, Smoothing Kernel=Uniform2026.050.56520.06120.03780.06140.5685
ConquerNetModel=Model A, Smoothing Kernel=Epanechnikov2026.050.67330.0390.02220.03730.5913