Model Selection and Model Averaging读书介绍
| 类别 | 页数 | 译者 | 网友评分 | 年代 | 出版社 |
|---|---|---|---|---|---|
| 书籍 | 332页 | 2020 | Cambridge University Press |
| 定价 | 出版日期 | 最近访问 | 访问指数 |
|---|---|---|---|
| GBP 64.99 | 2020-02-20 … | 2020-06-22 … | 92 |
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 model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model sel...
作者简介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 model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled, with discussions of frequentist and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R code.
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