Model selection and model averaging
Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed Hamlet actually a good writer?" "Choosing a suitable model is central to all statistical work with...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Cambridge ; New York :
Cambridge University Press
2008.
|
| Colección: | CUP ebooks.
Cambridge series in statistical and probabilistic mathematics. |
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b39738140*spi |
Tabla de Contenidos:
- Model selection : data examples and introduction
- Akaike's information criterion
- The Bayesian information criterion
- A comparison of some selection methods
- Bigger is not always better
- The focussed information criterion
- Frequentist and Bayesian model averaging
- Lack-of-fit and goodness-of-fit tests
- Model selection and averaging schemes in action
- Further topics.
