Quantile Regression on Scenario 2 (n=5000)
0.143MSE (τ=0.05)ConquerNet
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| ConquerNetModel=Model B, Kernel=Gaussian2026.05 | 0.143 | 0.0237 | 0.0195 | 0.0284 | 0.1835 | |
| ConquerNetModel=Model B, Kernel=Uniform2026.05 | 0.1445 | 0.0215 | 0.018 | 0.0318 | 0.1884 | |
| ConquerNetModel=Model B, Kernel=Epanechnikov2026.05 | 0.1682 | 0.0235 | 0.0179 | 0.0268 | 0.1823 | |
| ConquerNetModel=Model B, Smoothing Kernel=Uniform2026.05 | 0.1706 | 0.0216 | 0.0176 | 0.0241 | 0.2239 | |
| BaselineModel=Model B2026.05 | 0.1732 | 0.0257 | 0.0178 | 0.03 | 0.1966 | |
| ConquerNetModel=Model B, Smoothing Kernel=Gaussian2026.05 | 0.18 | 0.0246 | 0.0155 | 0.0245 | 0.2157 | |
| BaselineModel=Model B2026.05 | 0.1847 | 0.0261 | 0.0205 | 0.0335 | 0.2108 | |
| ConquerNetModel=Model A, Kernel=Epanechnikov2026.05 | 0.1907 | 0.0265 | 0.0232 | 0.0302 | 0.2563 | |
| ConquerNetModel=Model A, Kernel=Uniform2026.05 | 0.2 | 0.024 | 0.0209 | 0.0353 | 0.2544 | |
| ConquerNetModel=Model A, Kernel=Gaussian2026.05 | 0.2076 | 0.026 | 0.0223 | 0.0308 | 0.2394 | |
| ConquerNetModel=Model B, Smoothing Kernel=Epanechnikov2026.05 | 0.2151 | 0.0263 | 0.0159 | 0.0289 | 0.1802 | |
| ConquerNetModel=Model A, Smoothing Kernel=Gaussian2026.05 | 0.2202 | 0.0276 | 0.0169 | 0.0273 | 0.2598 | |
| BaselineModel=Model A2026.05 | 0.229 | 0.0315 | 0.0244 | 0.0354 | 0.2551 | |
| ConquerNetModel=Model A, Smoothing Kernel=Uniform2026.05 | 0.2308 | 0.0286 | 0.0181 | 0.0275 | 0.2586 | |
| ConquerNetModel=Model A, Smoothing Kernel=Epanechnikov2026.05 | 0.2415 | 0.0284 | 0.0177 | 0.0265 | 0.2492 | |
| BaselineModel=Model A2026.05 | 0.2752 | 0.0275 | 0.0222 | 0.0308 | 0.2747 |