Commentary: practical advantages of Bayesian analysis of epidemiologic data.
By David B. Dunson. American Journal of Epidemiology, 2001
371 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/42ae18b0427af3e25a3a1de20b939790384a0ef6
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Citations in the corpus
- Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling. (2005, methodology): “Because the review article of Enøe et al. (2000) described many frequentist and Bayesian diagnostic-test modeling strategies, we focused on the computational aspects involved in a fully Bayesian analysis to estimate parameters of interest from these models.”
- On Using Bayesian Methods to Address Small Sample Problems (2016, methodology): “…that MCMCmethods are better equipped to model data with small samples because MCMC does not rely on asymptotics in the same way that frequentist methods do (Depaoli & van de Schoot, 2015; Dunson, 2001; Kadane, 2015; Lee & Song, 2004; Muthén & Asparouhov, 2012; van de Schoot et al., 2014).”
- Spatial and spatio-temporal models with R-INLA. (2013, positive): “Under these circumstances, the Bayesian approach is generally particularly effective (Dunson, 2001) and has been applied in several epidemiological applications, from ecology (Clark, 2005) to environmental studies (Wikle, 2003; Clark and Gelfand, 2006), to infectious disease (Jewell et al.”
- Spatial and spatio-temporal models with R-INLA. (2013, background): “Under these circumstances, the Bayesian approach is generally particularly effective (Dunson, 2001) and has been applied in several epidemiological applications, from ecology (Clark, 2005) to environmental studies (Wikle, 2003; Clark and Gelfand, 2006), to infectious disease (Jewell et al., 2009).”
- Bayesian data analysis. (2010, background): “The advantages of Bayesian analysis have been recognized by many scientific disciplines, including archeology (Litton & Buck, 1995), astrophysics (Loredo, 1992), conservation biology (Wade, 2000), ecology (Ellison, 2004), epidemiology (Dunson, 2001), evolutionary biology (Huelsenbeck, Ronquist, Niel”