Bayesian Semiparametric Isotonic Regression for Count Data

By David B. Dunson. 2005

61 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fe47cbe715891ff2b9e7faf9ac6628398aeeac4d

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Citations in the corpus

A simple monotone process with application to radiocarbon‐dated depth chronologies (2008, background): “These arise in applications as diverse as price effect data (e.g. Brezger and Steiner (2005)), leukaemia prognosis (Holmes and Heard, 2003), sleeping problems (Dunson, 2005), dose–response (Kong and Eubank, 2006) and growth curves (e.g. Ramsay (1998)).”
Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes (2008, background): “Dunson is Senior Investigator, Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709 (E-mail: dunson1@niehs.nih.gov). Amy H. Herring is Associate Professor of Biostatistics, University of North Carolina, Chapel Hill, NC 27599. Stephanie M. Engel ”
Shape constrained additive models (2015, methodology): “Monotonic regression within the Bayesian framework has been considered by Lang and Brezger (2004), Holmes and Heard (2003), Dunson and Neelon (2003), and Dunson (2005).”
The Nested Dirichlet Process (2008, background): “…applications of the DP include finance (Kacperczyk, Damien, and Walker 2003), econometrics (Chib and Hamilton 2002; Hirano 2002), epidemiology (Dunson 2005), genetics (Medvedovic and Sivaganesan 2002; Dunson, Herring, and Mulheri-Engel 2007a), medicine (Kottas, Branco, and Gelfand 2002;…”
Bayesian dynamic density estimation (2008, background): “Some examples of recent applications of the Dirichlet processes include finance, econometrics, epidemiology, genetics and survival analysis among others (Müller et al. 2004; Kacperczyk et al. 2004; Chib and Hamilton 2002; Hirano 2002; Dunson 2005; Medvedovic and Sivaganesan 2002; Dunson et al. 2007a”
A variable selection approach to monotonic regression with Bernstein polynomials (2011, methodology): “To choose a value for M in finite samples, we take an approach similar to the approach taken by Neelon and Dunson [38] for choosing the number of knots.”