Quantile Regression on Scenario 2 n=10000
0.1008MSE (τ=0.05)ConquerNet
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| ConquerNetModel=Model B, Kernel=Gaussian2026.05 | 0.1008 | 0.0152 | 0.012 | 0.0187 | 0.1036 | |
| ConquerNetModel=Model B, Smoothing Kernel=Epanechnikov2026.05 | 0.1038 | 0.0146 | 0.0107 | 0.0173 | 0.1302 | |
| ConquerNetModel=Model B, Kernel=Uniform2026.05 | 0.1043 | 0.0149 | 0.0113 | 0.0182 | 0.1074 | |
| ConquerNetModel=Model B, Kernel=Epanechnikov2026.05 | 0.1049 | 0.0143 | 0.0116 | 0.0203 | 0.1154 | |
| ConquerNetModel=Model B, Smoothing Kernel=Gaussian2026.05 | 0.1085 | 0.016 | 0.0119 | 0.0178 | 0.1144 | |
| ConquerNetModel=Model A, Kernel=Uniform2026.05 | 0.1136 | 0.0168 | 0.0137 | 0.0188 | — | |
| ConquerNetModel=Model B, Smoothing Kernel=Uniform2026.05 | 0.1172 | 0.0151 | 0.0118 | 0.0175 | 0.1342 | |
| ConquerNetModel=Model A, Kernel=Epanechnikov2026.05 | 0.1227 | 0.0168 | 0.0137 | 0.0197 | 0.1233 | |
| BaselineModel=Model B2026.05 | 0.1254 | 0.0174 | 0.0142 | 0.0204 | 0.1302 | |
| BaselineModel=Model A2026.05 | 0.1285 | 0.0213 | 0.0157 | 0.0231 | 0.1723 | |
| ConquerNetModel=Model A, Kernel=Gaussian2026.05 | 0.1302 | 0.0152 | 0.0134 | 0.0187 | 0.1354 | |
| ConquerNetModel=Model A, Smoothing Kernel=Gaussian2026.05 | 0.1316 | 0.0176 | 0.0128 | 0.0193 | 0.1537 | |
| BaselineModel=Model B2026.05 | 0.1323 | 0.0202 | 0.0129 | 0.022 | 0.1358 | |
| ConquerNetModel=Model A, Smoothing Kernel=Epanechnikov2026.05 | 0.143 | 0.0162 | 0.0126 | 0.0191 | 0.1388 | |
| ConquerNetModel=Model A, Smoothing Kernel=Uniform2026.05 | 0.1457 | 0.018 | 0.0128 | 0.0191 | 0.1467 | |
| BaselineModel=Model A2026.05 | 0.1704 | 0.0205 | 0.0145 | 0.0223 | 0.167 |