Estimation for multivariate normal and Student-t data with
monotone missingness
Description
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