R package REBayes: Empirical Bayes Estimation and Inference in R. Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization.
Keywords for this software
References in zbMATH (referenced in 11 articles , 1 standard article )
Showing results 1 to 11 of 11.
- Banerjee, Trambak; Liu, Qiang; Mukherjee, Gourab; Sun, Wengunag: A general framework for empirical Bayes estimation in discrete linear exponential family (2021)
- Xing, Zhengrong; Carbonetto, Peter; Stephens, Matthew: Flexible signal denoising via flexible empirical Bayes shrinkage (2021)
- Jiang, Wenhua: On general maximum likelihood empirical Bayes estimation of heteroscedastic IID normal means (2020)
- Koenker, Roger; Gu, Jiaying: Comment: Minimalist (g)-modeling (2019)
- Feng, Long; Dicker, Lee H.: Approximate nonparametric maximum likelihood for mixture models: a convex optimization approach to fitting arbitrary multivariate mixing distributions (2018)
- Madrid-Padilla, Oscar-Hernan; Polson, Nicholas G.; Scott, James: A deconvolution path for mixtures (2018)
- Youngseok Kim, Peter Carbonetto, Matthew Stephens, Mihai Anitescu: A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming (2018) arXiv
- Roger Koenker; Jiaying Gu: REBayes: An R Package for Empirical Bayes Mixture Methods (2017) not zbMATH
- Zhao, Sihai Dave: Integrative genetic risk prediction using non-parametric empirical Bayes classification (2017)
- Koenker, Roger; Mizera, Ivan: Convex optimization, shape constraints, compound decisions, and empirical Bayes rules (2014)
- Roger Koenker and Ivan Mizera: Convex Optimization in R (2014) not zbMATH