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
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Lykou, Anastasia; Ntzoufras, Ioannis: On Bayesian lasso variable selection and the specification of the shrinkage parameter (2013)
- Taddy, Matt: Multinomial inverse regression for text analysis (2013)
- Karabatsos, George; Walker, Stephen G.: Adaptive-modal Bayesian nonparametric regression (2012)
- 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)