Quantile Regression on Scenario 3 n=5000
0.8839MSE (τ=0.05)ConquerNet
Evaluation Results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| ConquerNetModel=Model B, Kernel=Epanechnikov2026.05 | 0.8839 | 0.3262 | 0.2112 | 0.3211 | 0.9264 | |
| ConquerNetModel=Model B, Kernel=Uniform2026.05 | 0.9431 | 0.3394 | 0.2002 | — | 0.8535 | |
| BaselineModel=Model B2026.05 | 0.9813 | 0.333 | 0.2148 | 0.3146 | 0.9632 | |
| ConquerNetModel=Model B, Kernel=Gaussian2026.05 | 0.9965 | 0.3196 | 0.213 | 0.3027 | 0.8176 | |
| ConquerNetModel=Model B, Smoothing Kernel=Epanechnikov2026.05 | 1.0053 | 0.375 | 0.2297 | 0.3473 | — | |
| BaselineModel=Model B2026.05 | 1.0061 | 0.3596 | 0.2196 | 0.3551 | 1.099 | |
| ConquerNetModel=Model B, Smoothing Kernel=Gaussian2026.05 | 1.0602 | 0.4263 | 0.2304 | 0.3525 | 1.0681 | |
| ConquerNetModel=Model A, Kernel=Uniform2026.05 | 1.1147 | 0.3521 | 0.2104 | 0.3096 | 1.117 | |
| ConquerNetModel=Model A, Kernel=Gaussian2026.05 | 1.1222 | 0.3339 | 0.2143 | 0.3045 | 0.9996 | |
| ConquerNetModel=Model A, Smoothing Kernel=Epanechnikov2026.05 | 1.1229 | 0.3615 | 0.198 | 0.3112 | 1.3312 | |
| ConquerNetModel=Model A, Smoothing Kernel=Uniform2026.05 | 1.1577 | 0.3449 | 0.1912 | 0.3082 | 1.2392 | |
| BaselineModel=Model A2026.05 | 1.1872 | 0.3741 | 0.2298 | 0.3626 | 1.1419 | |
| ConquerNetModel=Model B, Smoothing Kernel=Uniform2026.05 | 1.2056 | 0.4038 | 0.232 | 0.3373 | 1.0528 | |
| ConquerNetModel=Model A, Kernel=Epanechnikov2026.05 | 1.2444 | 0.352 | 0.2145 | 0.3247 | 1.2093 | |
| BaselineModel=Model A2026.05 | 1.287 | 0.3854 | 0.2146 | 0.3387 | 1.3495 | |
| ConquerNetModel=Model A, Smoothing Kernel=Gaussian2026.05 | 1.3139 | 0.3379 | 0.1993 | 0.3269 | 1.1897 |