monomvn-package {monomvn} | R Documentation |
Estimation of multivariate normal 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 an (almost) arbitrary amount of missing data. The current version supports maximum likelihood inference and beta implementation of a Bayesian version employing a Bayesian lasso. A fully functional standalone (beta) interface to the Bayesian lasso (from Park & Casella) and ridge regression with model selection via Reversible Jump is also provided
For a fuller overview including a complete list of functions, demos and
vignettes, please use help(package="tgp")
.
Robert B. Gramacy bobby@statslab.cam.ac.uk
Maintainer: Robert B. Gramacy bobby@statslab.cam.ac.uk
Robert B. Gramacy and Joo Hee Lee (2007).
On estimating covariances between many assets with histories
of highly variable length. Preprint available on arXiv:0710.5837:
http://arxiv.org/abs/0710.5837
http://www.statslab.cam.ac.uk/~bobby/monomvn.html
monomvn
, norm, mvnmle