Tlasso: Non-Convex Optimization and Statistical Inference for Sparse...

In Tlasso: Non-Convex Optimization and Statistical Inference for Sparse Tensor Graphical Models

Description

An optimal alternating optimization algorithm for estimation of precision matrices of sparse tensor graphical models, and an efficient inference procedure for support recovery of the precision matrices.

Details

Package:	Tlasso
Type:	Package
Date	2016-09-17
License:	GPL (>= 2)

Author(s)

Xiang Lyu, Will Wei Sun, Zhaoran Wang, Han Liu, Jian Yang, Guang Cheng.

Maintainer: Xiang Lyu <[e-mail]>

References

Fan J, Feng Y, Wu Y. Network exploration via the adaptive LASSO and SCAD penalties. The annals of applied statistics, 2009, 3(2): 521.

Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 2008: 9.3: 432-441.

Lee W, Liu Y. Joint estimation of multiple precision matrices with common structures. Journal of Machine Learning Research, 2015, 16: 1035-1062.

Li H, Gui J. Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks. Biostatistics, 2006, 7(2): 302-317.

Lyu X, Sun W, Wang Z, Liu H, Yang J, Cheng G. Tensor Graphical Model: Non-convex Optimization and Statistical Inference. IEEE transactions on pattern analysis and machine intelligence, 2019, 42(8): 2024-2037.

Tlasso documentation built on Feb. 1, 2022, 9:07 a.m.