Quantile Regression on Scenario 1 n=10000
0.0618MSE (τ=0.05)Baseline
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| BaselineModel=Model A2026.05 | 0.0618 | 0.0067 | 0.0047 | 0.0063 | 0.1366 | |
| ConquerNetModel=Model A, Kernel=Epanechnikov2026.05 | 0.0627 | 0.0254 | 0.0127 | 0.0094 | 0.0434 | |
| ConquerNetModel=Model A, Kernel=Gaussian2026.05 | 0.0641 | 0.0245 | 0.0122 | 0.01 | 0.0465 | |
| ConquerNetModel=Model A, Smoothing Kernel=Epanechnikov2026.05 | 0.0653 | 0.0061 | 0.0037 | 0.0053 | 0.0665 | |
| BaselineModel=Model A2026.05 | 0.0661 | 0.015 | 0.0088 | 0.0076 | 0.0596 | |
| ConquerNetModel=Model A, Smoothing Kernel=Gaussian2026.05 | 0.0678 | 0.0064 | 0.0034 | 0.0055 | 0.0625 | |
| ConquerNetModel=Model A, Kernel=Uniform2026.05 | 0.0689 | 0.0239 | 0.0127 | 0.0099 | 0.0483 | |
| ConquerNetModel=Model B, Kernel=Uniform2026.05 | 0.0769 | 0.0275 | 0.0153 | 0.0123 | 0.044 | |
| BaselineModel=Model B2026.05 | 0.0783 | 0.02 | 0.0121 | 0.0118 | 0.0455 | |
| ConquerNetModel=Model A, Smoothing Kernel=Uniform2026.05 | 0.0789 | 0.0058 | 0.0036 | 0.0057 | 0.0543 | |
| ConquerNetModel=Model B, Kernel=Epanechnikov2026.05 | 0.081 | 0.0269 | 0.0157 | 0.0113 | 0.0379 | |
| ConquerNetModel=Model B, Kernel=Gaussian2026.05 | 0.0829 | 0.0276 | 0.0156 | 0.011 | 0.0443 | |
| ConquerNetModel=Model B, Smoothing Kernel=Uniform2026.05 | 0.0974 | 0.0093 | 0.0055 | 0.008 | 0.0736 | |
| ConquerNetModel=Model B, Smoothing Kernel=Gaussian2026.05 | 0.1062 | 0.0095 | 0.0055 | 0.0086 | 0.0726 | |
| ConquerNetModel=Model B, Smoothing Kernel=Epanechnikov2026.05 | 0.1294 | 0.0089 | 0.0057 | 0.0092 | 0.0663 | |
| BaselineModel=Model B2026.05 | 0.1691 | 0.0099 | 0.0066 | 0.0082 | 0.3882 |