sparseMatEst-package: Sparse Matrix Estimation and Inference

In sparseMatEst: Sparse Matrix Estimation and Inference

Description

The sparseMatEst library provides functions for estimating sparse covariance and precision matrices with error control.

Details

Given a data matrix, this package contains two main functions used to estimate the covariance matrix and the precision matrix of the data under the assumption of sparsity, that most off-diagonal entries are zero. This is achieved by selecting a false positive rate corresponding to the probability that a true zero entry is falsely chosen to be non-zero by the estimator.

The false positive rate can be treated as an interpretable penalization parameter. Setting this to zero will return a diagonal matrix. Choosing a false positive rate away from zero will allow for the estimator to contain some non-zero off-diagonal entries.

Future updates coming Fall 2019 include inferential tools based on these sparse matrix estimators. These include a variant of linear and quadratic discriminant analysis, fitting a Gaussian mixture assuming sparsity, network clustering algorithm, and a method to fit a random design linear regression model.

Author(s)

Adam B Kashlak [e-mail]

References

Kashlak, Adam B., and Linglong Kong. "A concentration inequality based methodology for sparse covariance estimation." arXiv preprint arXiv:1705.02679 (2017).

Kashlak, Adam B. "Non-asymptotic error controlled sparse high dimensional precision matrix estimation." arXiv preprint arXiv:1903.10988 (2019).

sparseMatEst documentation built on May 17, 2019, 9:03 a.m.