Model selection and model averaging
Bayesian statistical decision theory Mathematical models Mathematical statistics e-böcker
Cambridge University Press
2008
EISBN 9780511421235
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.
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 data. Selecting the variables for use in a regression model is one important example. The past two decades have seen rapid advances both in our ability to fit models and in the theoretical understanding of model selection needed to harness this ability, yet this book is the first to provide a synthesis of research from this active field, and it contains much material previously difficult or impossible to find. In addition, it gives practical advice to the researcher confronted with conflicting results." "Model choice criteria are explained, discussed and compared, including Akaike's information criterion AIC, the Bayesian information criterion BIC and the focused information criterion FIC. Importantly, the uncertainties involved with model selection are addressed, with discussions of frequentist and Bayesian methods. Finally, model averaging schemes, which combine the strengths of several candidate models, are presented."--Jacket.
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.
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 data. Selecting the variables for use in a regression model is one important example. The past two decades have seen rapid advances both in our ability to fit models and in the theoretical understanding of model selection needed to harness this ability, yet this book is the first to provide a synthesis of research from this active field, and it contains much material previously difficult or impossible to find. In addition, it gives practical advice to the researcher confronted with conflicting results." "Model choice criteria are explained, discussed and compared, including Akaike's information criterion AIC, the Bayesian information criterion BIC and the focused information criterion FIC. Importantly, the uncertainties involved with model selection are addressed, with discussions of frequentist and Bayesian methods. Finally, model averaging schemes, which combine the strengths of several candidate models, are presented."--Jacket.
