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

Description Details Author(s) References

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)

Will Wei Sun, Zhaoran Wang, Xiang Lyu, Han Liu, Guang Cheng.
Maintainer: Xiang Lyu <lyu17@purdue.edu>

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.
Sun W, Wang Z, Lyu X, Liu H, Cheng G. Sparse Tensor Graphical Model: Non-convex Optimization and Statistical Inference. 2016. arXiv:1609.04522.

Tlasso documentation built on May 29, 2017, 5:59 p.m.

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