References in zbMATH (referenced in 13 articles , 1 standard article )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Banerjee, Trambak; Liu, Qiang; Mukherjee, Gourab; Sun, Wengunag: A general framework for empirical Bayes estimation in discrete linear exponential family (2021)
  2. Jason Willwerscheid, Matthew Stephens: ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families (2021) arXiv
  3. Xing, Zhengrong; Carbonetto, Peter; Stephens, Matthew: Flexible signal denoising via flexible empirical Bayes shrinkage (2021)
  4. Jiang, Wenhua: On general maximum likelihood empirical Bayes estimation of heteroscedastic IID normal means (2020)
  5. Kim, Youngseok; Carbonetto, Peter; Stephens, Matthew; Anitescu, Mihai: A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming (2020)
  6. Koenker, Roger; Gu, Jiaying: Comment: Minimalist (g)-modeling (2019)
  7. Feng, Long; Dicker, Lee H.: Approximate nonparametric maximum likelihood for mixture models: a convex optimization approach to fitting arbitrary multivariate mixing distributions (2018)
  8. Madrid-Padilla, Oscar-Hernan; Polson, Nicholas G.; Scott, James: A deconvolution path for mixtures (2018)
  9. Youngseok Kim, Peter Carbonetto, Matthew Stephens, Mihai Anitescu: A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming (2018) arXiv
  10. Roger Koenker; Jiaying Gu: REBayes: An R Package for Empirical Bayes Mixture Methods (2017) not zbMATH
  11. Zhao, Sihai Dave: Integrative genetic risk prediction using non-parametric empirical Bayes classification (2017)
  12. Koenker, Roger; Mizera, Ivan: Convex optimization, shape constraints, compound decisions, and empirical Bayes rules (2014)
  13. Roger Koenker and Ivan Mizera: Convex Optimization in R (2014) not zbMATH