A Bayesian Semiparametric Model for Random-Effects Meta-Analysis
By Deborah L Burr, Hani Doss. 2005
90 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a93fdd52804e29fccfe302328c0bd4c858703bdd
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In Statistics and Probability
- Julian P. T. Higgins
- Omiros Papaspiliopoulos
- S. Thompson
- Gareth O. Roberts
- Lawrence Carin
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Citations in the corpus
- Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models (2007, background): “…density estimation (M¨uller et al., 1996), survival analysis (Gelfand & Kottas, 2003), semi-parametric analysis of variance (M¨uller et al., 2005), cluster analysis and partition modelling (Quintana & Iglesias, 2003), meta-analysis (Burr & Doss, 2005) and machine learning (Teh et al., 2006).”
- Multi-Task Learning for Classification with Dirichlet Process Priors (2007, methodology): “1 DIRICHLET PROCESSES FOR NONPARAMETRIC HIERARCHICAL BAYESIAN MODELING Hierarchical Bayesian modeling is one of the most important methods for meta analysis (Burr and Doss., 2005; Dominici et al., 1997; Hoff, 2003; Müller et al., 2004; Mallick and Walker, 1997).”
- A re-evaluation of random-effects meta-analysis (2009, methodology): “Bayesian semiparametric random-effects distributions have been proposed (Burr et al., 2003; Burr and Doss, 2005; Ohlssen et al., 2007) based on Dirichlet process priors.”
- Semi-Supervised Multitask Learning (2007, methodology): “Hierarchical Bayesian modeling is one of the most important methods for meta analysis [23], [24], [25], [26], [27].”
- Bayesian Semiparametric Structural Equation Models with Latent Variables (2010, methodology): “Burr and Doss (2005) used a conditional DP for the random effects distribution within a meta analysis application.”
- Multitask Compressive Sensing (2009, background): “[ 27 ]‐[31]. Hierarchical Bayesian models provide the flexibility to model both the individuality of tasks”
- The Matrix Stick-Breaking Process (2008, background): “The conditional DP approach of Burr and Doss (2005) also does not address this problem.”