Similarity of samples and trimming
We say that two probabilities are similar at level a if they are contaminated versions (up to an a fraction) of the same common probability. We show how this model is related to minimal distances between sets of trimmed probabilities. Empirical versions turn out to present an overfitting effect in t...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/29685 |
| Acceso en línea: | https://hdl.handle.net/10902/29685 |
| Access Level: | acceso abierto |
| Palabra clave: | Asymptotics Bootstrap Consistency Mass transportation problem Over-fitting Robustness Similarity of distributions Trimmed probability Wasserstein distance |
| Sumario: | We say that two probabilities are similar at level a if they are contaminated versions (up to an a fraction) of the same common probability. We show how this model is related to minimal distances between sets of trimmed probabilities. Empirical versions turn out to present an overfitting effect in the sense that trimming beyond the similarity level results in trimmed samples that are closer than expected to each other. We show how this can be combined with a bootstrap approach to assess similarity from two data samples. |
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