Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach

By Joyee Ghosh, Merlise A. Clyde. 2011

48 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Control functionals for Monte Carlo integration (2014, background): “…quasi- Monte-Carlo (RQMC) sampling (Dick and Pillichshammer, 2010), (d) Rao–Blackwellization (Robert and Casella, 2004; Douc and Robert, 2011; Ghosh and Clyde, 2011; Olsson and Ryden, 2011), (e) Riemann sums (Philippe, 1997), (f) control variates (Glasserman, 2004; Mira et al., 2013; Li…”
Predicting the present with Bayesian structural time series (2014, background): “Improvements to SSVS have been proposed by several authors, notably Ghosh and Clyde (2011).”
A guide to Bayesian model selection for ecologists (2015, background): “More recently, a form of data augmentation has been proposed to generalize these methods for cases where the design matrix is nonorthogonal (Ghosh and Clyde 2011).”
Bayesian Variable Selection Under Collinearity (2015, methodology): “Letting X denote the design matrix under the full model, we assume that the columns of X have been centered to have mean 0 and scaled so that the norm of each column is √ n, as in Ghosh and Clyde (2011).”
Weighted‐Average Least Squares (WALS): A Survey (2016, methodology): “Such approximations can be obtained through the Laplace method for integrals (Tierney and Kadane, 1986), the output of some MCMC method (Han and Carlin, 2001; Ghosh and Clyde, 2011), or a suitable combination of these two methods (DiCiccio et al., 1997; Lewis and Raftery, 1997).”
On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression (2015, background): “…distributions for regression coefficients frequently arise in Bayesian variable selection, for regression models with binary or categorical response variables (Holmes and Held, 2006; Mitra and Dunson, 2010; Ghosh and Clyde, 2011; Ghosh et al., 2011; Ghosh and Reiter, 2013; Li and Clyde, 2015).”
Mixtures of g-Priors in Generalized Linear Models (2015, methodology): “Furthermore, in order to compare with the Rao-Blackwellization estimate under independent normal priors implemented using the orthogonal data augmentation (RB-ODA) proposed by Ghosh and Clyde (2011), we also fit a probit regression under the CH-g prior.”