<span>A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best rep
Model Selection and Inference: A Practical Information-Theoretic Approach
β Scribed by Kenneth P. Burnham, David R. Anderson (auth.)
- Publisher
- Springer New York
- Year
- 1998
- Tongue
- English
- Leaves
- 373
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Front Matter....Pages i-xx
Introduction....Pages 1-31
Information Theory and Log-Likelihood Models: A Basis for Model Selection and Inference....Pages 32-74
Practical Use of the Information-Theoretic Approach....Pages 75-117
Model-Selection Uncertainty with Examples....Pages 118-158
Monte Carlo and Example-Based Insights....Pages 159-229
Statistical Theory....Pages 230-314
Summary....Pages 315-328
Back Matter....Pages 329-355
β¦ Subjects
Statistical Theory and Methods
π SIMILAR VOLUMES
<p><p>The variety and increasing availability of hypermedia information systems, which are used in stationary applications like operatorsβ consoles as well as mobile systems, e.g. driver information and navigation systems in automobiles form a foundation for the mediatization of the society. From th
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset o