Objective Bayesian Methods for Model Selection: Introduction and Comparison

By James O. Berger, Luis R. Pericchi. 2001

429 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/26155bb0743237249f6cda15dd96df6ec464635e

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Citations in the corpus

Model Selection (2018, methodology): “The Bayesian approach to model selection is discussed in Hoeting et al. (1999) and Berger and Pericchi (2001).”
Compatibility of prior specifications across linear models (2008, methodology): “Some authors have raised criticism against the use of g-priors for model selection (see for a clear exposition Berger and Pericchi, 2001), and have suggested alternative conventional priors, such as the Cauchy prior by Zellner and Siow (1980), recently discussed in Bayarri and Garcia-Donato (2007).”
Bayes factors based on test statistics (2005, methodology): “Recent developments on the use of Bayes factors for model selection and objective interpretations are summarized in Rao and Wu (2001), Chipman et al. (2001), Berger and Pericchi (2001) and in accompanying discussion in Lahiri (2001).”
An exploration of aspects of Bayesian multiple testing (2006, background): “It follows, from (1) and (4), that C1 can be written”
Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417 (2012, background): “With permission.) model M k as , Berger and Pericchi (2001), Kass and Raftery (1995), Ghosh and Samanta (2001), Berger and Pericchi (2004), and Barbieri and Berger (2004).”
Bayesian hypothesis testing for psychologists: a tutorial on the Savage-Dickey method. (2010, positive): “Other advantages of Bayesian hypothesis testing include the following (Wagenmakers, Lee, Lodewyckx, & Iverson, 2008; see also Berger & Pericchi, 2001; Dennis, Lee, & Kinnell, 2008; Kass & Raftery, 1995): 1.”
Generalized Fiducial Inference: A Review and New Results (2016, background): “As discussed in Berger and Pericchi (2001), one of the issues with the use of improper priors in Bayesian model selection is the presence of arbitrary scaling constant.”
Extended Bayesian information criteria for model selection with large model spaces (2008, methodology): “The original bic is an approximate Bayes approach, see Berger & Pericchi (2001) and some details later in this article.”
Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem (2010, background): “See Berger and Pericchi (2001) and Liang et al.”
The Median Probability Model and Correlated Variables (2018, methodology): “First, using predictive matching arguments (Berger and Pericchi 2001; Bayarri et al. 2012; Fouskakis et al. 2018), we provide new arguments for the suitability of g-type priors as opposed to independent product priors.”
Model Averaging and Its Use in Economics (2017, background): “If the arbitrary constant relates to a parameter that is common to all models, it will simply cancel in the ratio (3), and for such parameters we can thus employ improper priors (Fernández et al., 2001a; Berger and Pericchi, 2001).”
Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective (2014, methodology): “Bayarri et al. (2012) motivated the use of such priors from a testing perspective, using a variety of formal desiderata based on Jeffreys (1961) and Berger and Pericchi (2001), including consistency criteria, predictive matching criteria and invariance criteria.”
Testing hypotheses via a mixture estimation model (2014, methodology): “…discussed in more detail below, has led to the proliferation of alternative ad hoc solutions, where the data are either used twice (Aitkin, 1991, 2010; Gelman et al., 2013b) or split in artificial ways (O’Hagan, 1995; Berger and Pericchi, 1996; Berger et al., 1998; Berger and Pericchi, 2001).”
Confusion Over Measures of Evidence (p's) Versus Errors (α's) in Classical Statistical Testing (2003, methodology): “out without incorporating any external information (see Berger 2000), and in recent years the objective Bayesian methodology for hypothesistesting and model selectionhas experienced rapid development (Berger and Pericchi 2001).”