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
For a fuller overview including a complete list of functions, demos and
vignettes, please use
Robert B. Gramacy firstname.lastname@example.org
Maintainer: Robert B. Gramacy email@example.com
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
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