By Kenneth P. Burnham
A special and complete textual content at the philosophy of model-based facts research and process for the research of empirical information. The e-book introduces details theoretic methods and focuses serious realization on a priori modeling and the choice of an exceptional approximating version that most sensible represents the inference supported via the information. It includes numerous new methods to estimating version choice uncertainty and incorporating choice uncertainty into estimates of precision. An array of examples is given to demonstrate a variety of technical matters. The textual content has been written for biologists and statisticians utilizing types for making inferences from empirical info.
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Additional info for Model Selection and Inference: A Practical Information-Theoretic Approach
Then a new partition is selected, and this whole process is repeated hundreds or thousands of times. Also, then some criterion is chosen, such as minimum squared prediction error, as a basis for model selection. There are several variations on this theme, and it is a useful methodology (Craven and Wahba 1979, Burman 1989, Shao 1993, Zhang 1993a, and Hjorth 1994). These methods are quite computer intensive and tend to be impractical if more than about 15-20 models must be evaluated or if sample size is large.
Distance sampling theory (Buckland et a1. 1993) relies on models of the detection function and often employ semiparametric models. Parameters in these models are nearly always estimated using maximum likelihood. , geographic information systems). Stein and Corsten (1991) have shown how Kriging (perhaps the most widely used spatial technique) can be expressed as a least squares problem, and the development of Markov chain Monte Carlo methods such as the Gibbs sampler allow other forms ofspatial models to be fitted by least squares or maximum likelihood (Augustin et a1.
As we admit in the Preface, we do not expect everyone to share our conservative philosophy regarding the fundamental strategy of data analysis and inference. 4 Some Trends At the present time, nearly every analysis is done using a computer; thus biologists and people in other disciplines are increasingly using likelihood methods for more generalized analyses. Standard computer software packages allow likelihood methods to be used where LS methods have been used in the past. LS methods will see decreasing use and likelihood methods will see increasing use as we enter the twenty-first century.
Model Selection and Inference: A Practical Information-Theoretic Approach by Kenneth P. Burnham