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

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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.