BAYESIAN REGRESSION ANALYSIS WITH SCALE MIXTURES OF NORMALS

By C. Fernández, Mark F. J. Steel. Econometric Theory, 2000

83 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/acff8c500ec6836bec6c877afc5399233fb2f923

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Citations in the corpus

Phylogeography takes a relaxed random walk in continuous space and time. (2010, background): “Depending on prior choice on φ b , one can generate an extensive number of distributions (Fernández and Steel 2000).”
Geometric Ergodicity of van Dyk and Meng's Algorithm for the Multivariate Student's t Model (2004, unknown): “Bayesian regression models with errors of this form have been studied by many authors, including West (1984), Liu (1996), and Fernández and Steel (1999, 2000).”
Mixtures of g-priors for Bayesian model averaging with economic applications (2010, unknown): “(3) In other words, we can interpret the prior on g as a way of extending the normal prior in (2) to a scale mixture of normals, which has substantially more flexible tails—see Andrews and Mallows (1974) and Fernández and Steel (2000).”
Elastic Net Regression Modeling With the Orthant Normal Prior (2011, methodology): “Scale mixtures of normal distributions have been used extensively in Bayesian modeling (e.g., Andrews and Mallows 1974; West 1987; Carlin and Polson 1991; Fernández and Steel 2000; Liang et al. 2008; Carvalho, Polson, and Scott 2009; Polson and Scott 2011a, 2011b).”
Bayesian quantile inference (2003, background): “Then Efjbjrjyg exists if and only if n k > r Proof The proof follows from Corollary 1 in Fernandez and Steel (2000). j PROPOSITION A2 If pðb; sÞ / s 1 then Eðsr j yÞ exists if and only if n k > r Proof Since mþ ¼ 1 for the standard exponential mixing distribution, the proof follows from Corollary…”
A review of machine learning in processing remote sensing data for mineral exploration (2021, methodology): “in terms of uncertainty quantification, there is scope for using Bayesian regression analysis (Fernandez and Steel, 2000) with Bayesian linear and logistic regression (Genkin et al.”
On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression (2015, background): “Fernández and Steel (2000) investigated the propriety of the posterior distribution and the existence of posterior moments of regression and scale parameters for a linear regression model, with errors distributed as scale mixtures of normals, under the independence”