Monomvn

monomvn: Estimation for multivariate normal and Student-t data with monotone missingness. Estimation of multivariate normal and student-t data of arbitrary dimension where the pattern of missing data is monotone. Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference and a full Bayesian approach employing scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness patterns. A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided


References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
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  1. Guha, Sharmistha; Rodriguez, Abel: Bayesian regression with undirected network predictors with an application to brain connectome data (2021)
  2. Mickael Binois, Robert B. Gramacy: hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R (2021) not zbMATH
  3. Posch, Konstantin; Arbeiter, Maximilian; Pilz, Juergen: A novel Bayesian approach for variable selection in linear regression models (2020)
  4. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  5. Zhang, Hai; Wang, Puyu; Dong, Qing; Wang, Pu: Sparse Bayesian linear regression using generalized normal priors (2017)
  6. Linder, Daniel F.; Panchal, Viral; Samawi, Hani; Ryu, Duchwan: Balanced Bayesian LASSO for heavy tails (2016)
  7. Yuan, J.; Wei, G.: An efficient Monte Carlo EM algorithm for Bayesian Lasso (2014)
  8. Lykou, Anastasia; Ntzoufras, Ioannis: On Bayesian lasso variable selection and the specification of the shrinkage parameter (2013)
  9. Taddy, Matt: Multinomial inverse regression for text analysis (2013)
  10. Karabatsos, George; Walker, Stephen G.: Adaptive-modal Bayesian nonparametric regression (2012)
  11. Ghosh, Joyee; Clyde, Merlise A.: Rao-Blackwellization for Bayesian variable selection and model averaging in linear and binary regression: a novel data augmentation approach (2011)
  12. Gramacy, Robert B.; Pantaleo, Ester: Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing (2010)