Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors

By Jennifer A. Hoeting, David Madigan, Adrian E. Raftery, C. Volinsky. 1999

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Citations in the corpus

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, neutral): “Statistical Modelling 2010; 10(4): 421–439 parameter estimates (Hoeting et al., 1999).”
Obtaining Well Calibrated Probabilities Using Bayesian Binning (2015, positive): “However , model averaging is typically superior to model selection (Hoeting et al. 1999).”
Optimal Weight Choice for Frequentist Model Average Estimators (2011, background): “Reviews of the relevant Bayesian literature can be found in the works of Hoeting et al. (1999), Raftery, Madigan, and Hoeting (1997).”
Determinants of Economic Growth: Will Data Tell? (2010, background): “Detailed discussions of BMA can be found in Leamer (1978) and Hoeting et al. (1999) for example.”
Application of machine learning methods to spatial interpolation of environmental variables (2011, methodology): “Given the improved results of model averaging in other disciplines (Goswami and O'Connor, 2007; Hoeting et al., 1999; Marmion et al., 2009; Raftery et al., 2005), further tests on the effects of such model averaging should not be discouraged in future studies.”
Seven deadly sins of contemporary quantitative political analysis (2014, background): “Bayesian approaches are common in the consistently top-ranked – by impact factor – Political Analysis (for example Hoeting et al., 1999; Lock & Gelman, 2010; Montgomery & Nyhan, 2010; Alvarez, Bailey & Katz, 2011; Grimmer, 2011; Montgomery, Hollenbach & Ward, 2012) but not in more massmarket venues…”
Neural Decision Trees (2017, methodology): “The model evidence is given by look for a review in Hoeting et al. (1999).”
A Default Bayesian Hypothesis Test for ANOVA Designs (2012, methodology): “Another possibility is to apply model averaging and calculate an inclusion probability for each predictor over all possible models (Clyde 1999; Hoeting et al. 1999).”
Identifying a Minimal Class of Models for High-dimensional Data (2015, methodology): “This approach was suggested both for the frequentist, Hjort & Claeskens (2003), and for the Bayesian, Hoeting et al. (1999), statistician.”