Book contents
- Frontmatter
- Contents
- Preface
- A guide to notation
- 1 Model selection: data examples and introduction
- 2 Akaike's information criterion
- 3 The Bayesian information criterion
- 4 A comparison of some selection methods
- 5 Bigger is not always better
- 6 The focussed information criterion
- 7 Frequentist and Bayesian model averaging
- 8 Lack-of-fit and goodness-of-fit tests
- 9 Model selection and averaging schemes in action
- 10 Further topics
- Overview of data examples
- References
- Author index
- Subject index
1 - Model selection: data examples and introduction
Published online by Cambridge University Press: 05 September 2012
- Frontmatter
- Contents
- Preface
- A guide to notation
- 1 Model selection: data examples and introduction
- 2 Akaike's information criterion
- 3 The Bayesian information criterion
- 4 A comparison of some selection methods
- 5 Bigger is not always better
- 6 The focussed information criterion
- 7 Frequentist and Bayesian model averaging
- 8 Lack-of-fit and goodness-of-fit tests
- 9 Model selection and averaging schemes in action
- 10 Further topics
- Overview of data examples
- References
- Author index
- Subject index
Summary
This book is about making choices. If there are several possibilities for modelling data, which should we take? If multiple explanatory variables are measured, should they all be used when forming predictions, making classifications, or attempting to summarise analysis of what influences response variables, or will including only a few of them work equally well, or better? If so, which ones can we best include? Model selection problems arrive in many forms and on widely varying occasions. In this chapter we present some data examples and discuss some of the questions they lead to. Later in the book we come back to these data and suggest some answers. A short preview of what is to come in later chapters is also provided.
Introduction
With the current ease of data collection which in many fields of applied science has become cheaper and cheaper, there is a growing need for methods which point to interesting, important features of the data, and which help to build a model. The model we wish to construct should be rich enough to explain relations in the data, but on the other hand simple enough to understand, explain to others, and use. It is when we negotiate this balance that model selection methods come into play. They provide formal support to guide data users in their search for good models, or for determining which variables to include when making predictions and classifications.
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- Model Selection and Model Averaging , pp. 1 - 21Publisher: Cambridge University PressPrint publication year: 2008
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