Name
Abstract | ||
|---|---|---|
We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We prove the existence of an optimal MAPE model and we show the universal consistency of Empirical Risk Minimization based on the MAPE. We also show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression, and we apply this weighting strategy to kernel regression. The behavior of the MAPE kernel regression is illustrated on simulated data. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.12.114 | Neurocomputing |
Keywords | Field | DocType |
Mean Absolute Percentage Error,Empirical Risk Minimization,Consistency,Optimization,Kernel regression | Econometrics,Mean absolute percentage error,Weighting,Symmetric mean absolute percentage error,Regression,Regression analysis,Empirical risk minimization,Statistics,Mathematics,Kernel regression,Approximation error | Journal |
Volume | ISSN | Citations |
192 | 0925-2312 | 22 |
PageRank | References | Authors |
1.08 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnaud De Myttenaere | 1 | 27 | 2.74 |
| Boris Golden | 2 | 27 | 2.74 |
| Bénédicte Le Grand | 3 | 126 | 18.50 |
| Fabrice Rossi | 4 | 28 | 3.09 |