Quantile Regression on Scenario 2 n=1000
0.4515MSE (τ=0.05)ConquerNet
Evaluation Results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| ConquerNetModel=Model B, Smoothing Kernel=Uniform2026.05 | 0.4515 | 0.0717 | 0.0503 | 0.0935 | 0.6034 | |
| BaselineModel=Model B2026.05 | 0.493 | 0.0583 | 0.0493 | 0.084 | 0.5898 | |
| BaselineModel=Model B2026.05 | 0.521 | 0.0683 | 0.0651 | 0.0869 | 0.7145 | |
| ConquerNetModel=Model B, Smoothing Kernel=Epanechnikov2026.05 | 0.58 | 0.0723 | 0.0546 | 0.0819 | 0.7484 | |
| ConquerNetModel=Model B, Kernel=Epanechnikov2026.05 | 0.6414 | 0.0879 | 0.058 | 0.0868 | 0.5577 | |
| ConquerNetModel=Model B, Kernel=Uniform2026.05 | 0.644 | 0.0605 | 0.0523 | 0.0808 | 0.5217 | |
| ConquerNetModel=Model A, Kernel=Epanechnikov2026.05 | 0.6887 | 0.0827 | 0.0716 | 0.1185 | 0.6827 | |
| ConquerNetModel=Model B, Kernel=Gaussian2026.05 | 0.7154 | 0.07 | 0.0553 | 0.0827 | 0.5483 | |
| ConquerNetModel=Model B, Smoothing Kernel=Gaussian2026.05 | 0.7367 | 0.0639 | 0.05 | 0.0759 | 0.6273 | |
| ConquerNetModel=Model A, Kernel=Gaussian2026.05 | 0.7481 | 0.0704 | 0.0577 | 0.0906 | 0.7275 | |
| BaselineModel=Model A2026.05 | 0.7892 | 0.0801 | 0.0655 | 0.1017 | 0.7819 | |
| ConquerNetModel=Model A, Smoothing Kernel=Gaussian2026.05 | 0.7994 | 0.0711 | 0.0587 | 0.0778 | 1.1466 | |
| ConquerNetModel=Model A, Kernel=Uniform2026.05 | 0.819 | 0.0734 | 0.0647 | 0.1053 | 0.889 | |
| BaselineModel=Model A2026.05 | 0.8292 | 0.0868 | 0.0619 | 0.0839 | 0.7874 | |
| ConquerNetModel=Model A, Smoothing Kernel=Uniform2026.05 | 0.8892 | 0.0721 | 0.0471 | 0.0964 | 0.8566 | |
| ConquerNetModel=Model A, Smoothing Kernel=Epanechnikov2026.05 | 0.9192 | 0.0787 | 0.0522 | 0.1048 | 0.9074 |