AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons

@article{Burnham2010AICMS,
  title={AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons},
  author={Kenneth P. Burnham and David R. Anderson and Kathryn P Huyvaert},
  journal={Behavioral Ecology and Sociobiology},
  year={2010},
  volume={65},
  pages={23-35}
}
We briefly outline the information-theoretic (I-T) approaches to valid inference including a review of some simple methods for making formal inference from all the hypotheses in the model set (multimodel inference). The I-T approaches can replace the usual t tests and ANOVA tables that are so inferentially limited, but still commonly used. The I-T methods are easy to compute and understand and provide formal measures of the strength of evidence for both the null and alternative hypotheses… Expand

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References

SHOWING 1-10 OF 91 REFERENCES
A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion
TLDR
Akaike’s information criterion is provided, using recent examples from the behavioural ecology literature, a simple introductory guide to AIC: what it is, how and when to apply it and what it achieves. Expand
Kullback-Leibler information as a basis for strong inference in ecological studies
We describe an information-theoretic paradigm for analysis of ecological data, based on Kullback–Leibler information, that is an extension of likelihood theory and avoids the pitfalls of nullExpand
Multimodel Inference
The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate comparisons to the Bayesian informationExpand
Stepwise Model Fitting and Statistical Inference: Turning Noise into Signal Pollution
TLDR
This study amplifies previous warnings about using stepwise procedures and recommends that biologists refrain from applying these methods, by using a simple simulation design. Expand
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction
TLDR
This special issue examines the suitability of the IT method for analysing data with multiple predictors, which researchers encounter in the authors' field and brings together different viewpoints to aid behavioural ecologists in understanding the method. Expand
Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error
TLDR
It is shown that automated model selection techniques should not be relied on in the analysis of complex multivariable datasets, as this can lead to extreme biases when predictors are collinear, have strong effects but differ in their degree of measurement error. Expand
Model selection and multimodel inference : a practical information-theoretic approach
The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference). A philosophy isExpand
Data-Based Selection of an Appropriate Biological Model: The Key to Modern Data Analysis
TLDR
A proper model is fully supported by the data, and has enough parameters to avoid bias, but not too many that precision is lost (the Principle of Parsimony). Expand
Why do we still use stepwise modelling in ecology and behaviour?
TLDR
It is shown that stepwise regression allows models containing significant predictors to be obtained from each year's data, and that the significance of the selected models vary substantially between years and suggest patterns that are at odds with those determined by analysing the full, 4-year data set. Expand
Information Criteria and Statistical Modeling
The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successfulExpand
...
1
2
3
4
5
...