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51,009
Bayesian Model Averaging for Linear Regression Models
 Journal of the American Statistical Association
, 1997
"... We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem in ..."
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Cited by 325 (17 self)
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involves averaging over all possible models (i.e., combinations of predictors) when making inferences about quantities of
Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests
, 2004
"... Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the sel ..."
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Cited by 407 (8 self)
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for the estimation of phylogenies and model parameters using all available models (modelaveraged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC
PACBayesian Model Averaging
 In Proceedings of the Twelfth Annual Conference on Computational Learning Theory
, 1999
"... PACBayesian learning methods combine the informative priors of Bayesian methods with distributionfree PAC guarantees. Building on earlier methods for PACBayesian model selection, this paper presents a method for PACBayesian model averaging. The main result is a bound on generalization error of a ..."
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Cited by 99 (3 self)
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PACBayesian learning methods combine the informative priors of Bayesian methods with distributionfree PAC guarantees. Building on earlier methods for PACBayesian model selection, this paper presents a method for PACBayesian model averaging. The main result is a bound on generalization error
Model Averaging
, 2010
"... Bayes rule for models A prior distribution over model space p(m) (or ‘hypothesis space’) can be updated to a posterior distribution after observing data y. This is implemented using Bayes rule p(my) = p(y m)p(m) p(y) where p(y m) is referred to as the evidence for model m and the denominator is ..."
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Bayes rule for models A prior distribution over model space p(m) (or ‘hypothesis space’) can be updated to a posterior distribution after observing data y. This is implemented using Bayes rule p(my) = p(y m)p(m) p(y) where p(y m) is referred to as the evidence for model m and the denominator
Frequentist model average estimators
 Journal of the American Statistical Association
, 2003
"... Abstract. The traditional use of model selection methods in practice is to proceed as if the final selected model had been chosen in advance, without acknowledging the additional uncertainty introduced by model selection. This often means underreporting of variability and too optimistic confidence ..."
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Cited by 71 (2 self)
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intervals. We build a general largesample likelihood apparatus in which limiting distributions and risk properties of estimatorspostselection as well as of model average estimators are precisely described, also explicitly taking modelling bias into account. This allows a drastic reduction of complexity
Benchmark Priors for Bayesian Model Averaging
 FORTHCOMING IN THE JOURNAL OF ECONOMETRICS
, 2001
"... In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, “diffuse” priors on modelspecific parameters can lead to quite unexpected consequ ..."
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Cited by 180 (5 self)
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In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, “diffuse” priors on modelspecific parameters can lead to quite unexpected
Least squares model averaging
 Econometrica
, 2007
"... This paper considers the problem of selection of weights for averaging across leastsquares estimates obtained from a set of models. Existing model average methods are based on exponential AIC and BIC weights. In distinction, this paper proposes selecting the weights by minimizing a Mallows ’ criteri ..."
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Cited by 52 (11 self)
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This paper considers the problem of selection of weights for averaging across leastsquares estimates obtained from a set of models. Existing model average methods are based on exponential AIC and BIC weights. In distinction, this paper proposes selecting the weights by minimizing a Mallows
Results 1  10
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51,009