Model Selection and Model Averaging

By Gerda Claeskens, Nils Lid Hjort. 2001

1,617 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/84167e961cbd110c371f7e0933d9b48db2a0aa49

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In Statistics and Probability

Citations in the corpus

Shapley values for feature selection: The good, the bad, and the axioms (2021, neutral): “In statistics, model averaging has been used to combine the strengths of several candidate selected models [1].”
Neural Network Attributions: A Causal Perspective (2019, methodology): “Bayesian model selection (Claeskens et al., 2008) is employed to determine the optimal order of the polynomial that best ﬁts the given data by maximizing the marginal likelihood.”
Jackknife model averaging for quantile regressions (2015, neutral): “For seminal contributions to BMA, see Draper (1995) and Raftery and Madigan, and Hoeting (1997); for an overview, see Hoeting, Madigan, Raftery, and Volinsky (1999). FMA has a relatively shorter history than BMA.”
Recent progress in road and lane detection: a survey (2014, background): “From a decision theoretic point of view, running all the algorithms and weighing their response is the best policy (as long as each of the algorithms has non zero probability to be correct in its assumptions) [61].”
Model Selection in Linear Mixed Models (2013, methodology): “In practical applications of statistical models (including linear mixed models), a key aspect of the analysis is often model selection, the choice of a particular model within a class of candidate models; see Claeskens and Hjort (2008) for a general review.”
The Bayesian information criterion: background, derivation, and applications (2012, methodology): “7 Some frequentist practitioners prefer the use of BIC over model selection criteria justiﬁed under frequentist principles, such as the Akaike information criterion (AIC).”
Generalized Least Squares Model Averaging (2016, methodology): “The book by Claeskens and Hjort (2008) provides an excellent overview of the literature on model selection and model averaging. Yuan and Yang (2005) and Zhang, Wan and Zhou (2012) discuss under what circumstances model averaging performs better than model selection. Hansen (2007) proposes a method o”
Differentially Private Model Selection via Stability Arguments and the Robustness of the Lasso (2013, background): “The statistical literature has considered a wide array of model selection procedures, many of which are proven consistent under very general assumptions; see Claeskens and Hjort (2008, Ch. 4) for a textbook introduction. The main new technical idea in our generic transformations is to identify effic”
AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS (2011, methodology): “These criteria may be used for model averaging (Claeskens and Hjort (2008)), or for selection of tuning parameters in shrinkage methods.”