Discrete choice modeling using Kernel Logistic Regression
The Kernel Logistic Regression is a popular technique in machine learning. In this work this technique is applied to the field of discrete choice modeling. This approach is equivalent to specifying non-parametric utilities in random utility models. A Monte Carlo simulation experiment has been carrie...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/30520 |
| Acceso en línea: | http://hdl.handle.net/10578/30520 |
| Access Level: | acceso abierto |
| Palabra clave: | Kernel Logistic Regression Random Utility Models Non-parametric Utilities Regresión logística del kernel Modelos de utilidad aleatorios Utilidades no paramétricas |
| Sumario: | The Kernel Logistic Regression is a popular technique in machine learning. In this work this technique is applied to the field of discrete choice modeling. This approach is equivalent to specifying non-parametric utilities in random utility models. A Monte Carlo simulation experiment has been carried out to compare this approach with Multinomial Logit models, comparing the goodness of fit and the capability of obtaining the specified utilities. |
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