Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

An R Package flare for High Dimensional Linear Regression and Precision Matrix Estimation

Language

English

Abstract

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, ℓqℓq Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling flexibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM). The package flare is coded in double precision C, and called from R by a user-friendly interface. The memory usage is optimized by using the sparse matrix output. The experiments show that flare is efficient and can scale up to large problems.

Keywords

sparse linear regression, sparse precision matrix estimation, alternating direction method of multipliers, robustness, tuning insensitiveness

Publication Date

2015

Source Publication Title

Journal of Machine Learning Research

Volume

16

Start Page

553

End Page

557

Link to Publisher's Edition

http://jmlr.org/papers/v16/li15a.html

ISSN (print)

15324435

ISSN (electronic)

15337928

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