monomvn-package {monomvn} | 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 the lasso (double-exponential) and Normal-Gamma priors, and Student-t errors. 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), 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")`.

### Author(s)

Robert B. Gramacy bobby@statslab.cam.ac.uk

Maintainer: Robert B. Gramacy bobby@statslab.cam.ac.uk

### References

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

http://www.statslab.cam.ac.uk/~bobby/monomvn.html

### See Also

`monomvn`, the now defunct `norm` package, mvnmle

[Package monomvn version 1.8 Index]