A review and comparison of four commonly used Bayesian and maximum likelihood model selection tools

By Eric J. Ward. 2008

194 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/76fe31cbfb7055e16e015d24fd0921c94a0acc16

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Using information theory as a substitute for stepwise regression in ecology and behavior (2010, unknown): “Furthermore, there are warnings in the literature that AIC or even maximum likelihood criteria as a whole cannot be safely used in case of nested models, mixed models, and correlated parameters (Vaida and Blanchard 2005; Nakagawa et al. 2007; Ward 2008), all of which are common in our field.”
Changing philosophies and tools for statistical inferences in behavioral ecology (2009, background): “Examples of other criteria include Bayesian information criterion (BIC) and deviance information criterion (DIC) (Congdon 2006; Claeskens and Hjort 2008; Ward 2008 ).”
A conceptual framework for the spatial analysis of landscape genetic data (2013, methodology): “More research is needed to develop and test approaches at model selection, e.g., using criteria such as AIC or BIC (Ward 2008) taking into account the inflated sample size in regression of distance matrices as well as inferential problems related to spatial autocorrelation in predictor and response…”
Performance of deviance information criterion model selection in statistical catch-at-age analysis (2008, methodology): “Although DIC has been applied in many studies (e.g., Barry et al., 2003; Kizilkaya and Tempelman, 2003), relatively few studies have evaluated the performance of DIC model selection (e.g., Cardoso and Tempelman, 2003; Kizilkaya and Tempelman, 2003, 2005; Berg et al., 2004; Ward, 2008).”
Quantifying the effects of prey abundance on killer whale reproduction (2009, methodology): “We examined the performance of Bayesian (Bayes factor, Kass & Raftery 1995) and likelihood (AIC, Burnham and Anderson 2002) model selection tools, as each has limitations (Link & Barker 2006; Ward 2008).”
Inverse modeling of dynamic nonequilibrium in water flow with an effective approach (2012, background): “…estimated parameters leads in most cases to an increased goodness of fit and therefore to smaller values of the RMSE, model selection criteria have been developed which seek to find a balance between model complexity and quality of the fit [Burnham and Anderson, 2002; Ye et al., 2008; Ward, 2008].”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, methodology): “Various procedures have been developed for this purpose, which rely on different strategies for model comparison and use different model selection criteria to estimate model fit relative to complexity (Forster 2000; Claeskens and Hjort 2008; Ward 2008).”