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## References

SHOWING 1-10 OF 11 REFERENCES

### Graph estimation from multi-attribute data

- Computer ScienceJ. Mach. Learn. Res.
- 2014

A new principled framework for estimating the structure of undirected graphical models from such multivariate (or multi-attribute) nodal data is proposed and a method that efficiently maximizes this objective is developed.

### High-dimensional graphs and variable selection with the Lasso

- Computer Science
- 2006

It is shown that neighborhood selection with the Lasso is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs and is hence equivalent to variable selection for Gaussian linear models.

### Sparse inverse covariance estimation with the graphical lasso.

- Computer ScienceBiostatistics
- 2008

Using a coordinate descent procedure for the lasso, a simple algorithm is developed that solves a 1000-node problem in at most a minute and is 30-4000 times faster than competing methods.

### Partial Correlation Estimation by Joint Sparse Regression Models

- Computer ScienceJournal of the American Statistical Association
- 2009

It is shown that space performs well in both nonzero partial correlation selection and the identification of hub variables, and also outperforms two existing methods.

### Model selection and estimation in the Gaussian graphical model

- Computer Science
- 2007

The implementation of the penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model is nontrivial, but it is shown that the computation can be done effectively by taking advantage of the efficient maxdet algorithm developed in convex optimization.

### Model Selection Through Sparse Maximum Likelihood Estimation

- Computer ScienceArXiv
- 2007

Two new algorithms for solving problems with at least a thousand nodes in the Gaussian case are presented, based on Nesterov's first order method, which yields a complexity estimate with a better dependence on problem size than existing interior point methods.

### A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees

- Computer Science, Mathematics
- 2013

This work proposes a new pseudolikelihood‐based graphical model selection method that aims to overcome some of the shortcomings of current methods, but at the same time retain all their respective strengths, and introduces a novel framework that leads to a convex formulation of the partial covariance regression graph problem, resulting in an objective function comprised of quadratic forms.

### Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization

- Mathematics
- 2001

We study the convergence properties of a (block) coordinate descent method applied to minimize a nondifferentiable (nonconvex) function f(x1, . . . , xN) with certain separability and regularity…

### Weak Convergence and Empirical Processes: With Applications to Statistics

- Mathematics
- 1996

This chapter discusses Convergence: Weak, Almost Uniform, and in Probability, which focuses on the part of Convergence of the Donsker Property which is concerned with Uniformity and Metrization.

### Weak convergence and empirical processes

- Mathematics
- 2019

1 Review of metric topology 3 1.1 Metric spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Open and closed sets . . . . . . . . . . . . . . . . . . . . . . . . . . . .…