Model averaging, missing data and multiple imputation: a case study for behavioural ecology

By Shinichi Nakagawa, R. Freckleton. Behavioral Ecology and Sociobiology, 2010

272 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ca135f78c02194283062bb0f2d5b98f3064a0815

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Citations in the corpus

Using information theory as a substitute for stepwise regression in ecology and behavior (2010, unknown): “2010), and dealing with collinearity and missing observations during this process (Freckleton 2010; Nakagawa and Freckleton 2010).”
The evolution and function of pattern diversity in snakes (2013, methodology): “As data were unlikely to be missing at random (Rubin 1976; Nakagawa and Freckleton 2011), removing cases with missing data would lead to biased parameter estimates and reduce overall statistical power.”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, background): “Nakagawa and Freckleton (2010) investigate the effect of missing observations on the IT output in an example analysis.”
Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018, background): “Several previous reviews on model averaging in ecology and 53 evolution, focussed exclusively on ‘information-theoretical model averaging’ (Johnson 54 and Omland, 2004; Hobbs and Hilborn, 2006; Burnham et al., 2011; Freckleton, 2011; 55 Grueber et al., 2011; Nakagawa and Freckleton, 2011; Richards e”
Multimodel inference in ecology and evolution: challenges and solutions (2011, methodology): “There are two methods by which the estimate and error for each parameter are weighted (detailed in Burnham & Anderson, 2002; Nakagawa & Freckleton, 2010).”
A brief introduction to mixed effects modelling and multi-model inference in ecology (2018, methodology): “Nakagawa & Freckleton (2011) recommend multiple imputation (MI) as a mechanism for handling missing data, and highlight the ability of this technique for more accurate estimates, particularly for IT-AIC approaches.”
Model selection and model averaging in behavioural ecology: the utility of the IT-AIC framework (2010, methodology): “Our discussion recognises that model averaging remains an open research area, with further work required to understand fully its properties and limitations (Burnham and Anderson 2002, pp152–155; Stephens et al. 2007a; Freckleton 2010; Nakagawa and Freckleton 2010).”
A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion (2010, background): “There are ways around this problem, recently outlined by Nakagawa and Freckleton (2008, 2010) , including modelbased augmentation and multiple imputation methods.”