R: Estimation for Multivariate Normal and Student-t Data with...
monomvn-package
R Documentation
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), the 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
Details
For a fuller overview including a complete list of functions, demos and
vignettes, please use help(package="monomvn").
Robert B. Gramacy, Joo Hee Lee and Ricardo Silva (2008).
On estimating covariances between many assets with histories
of highly variable length. Preprint available on arXiv:0710.5837:
http://arxiv.org/abs/0710.5837