Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression

@article{Aragam2015LearningDA,
  title={Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression},
  author={Bryon Aragam and Arash A. Amini and Qing Zhou},
  journal={ArXiv},
  year={2015},
  volume={abs/1511.08963}
}
We study a family of regularized score-based estimators for learning the structure of a directed acyclic graph (DAG) for a multivariate normal distribution from high-dimensional data with $p\gg n$. Our main results establish support recovery guarantees and deviation bounds for a family of penalized least-squares estimators under concave regularization without assuming prior knowledge of a variable ordering. These results apply to a variety of practical situations that allow for arbitrary… CONTINUE READING
5
Twitter Mentions

References

Publications referenced by this paper.
SHOWING 1-10 OF 55 REFERENCES

A Selective Review of Group Selection in High-Dimensional Models.

  • Statistical science : a review journal of the Institute of Mathematical Statistics
  • 2012
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

A general theory of concave regularization for high-dimensional sparse estimation problems

C.-H. Zhang, T. Zhang
  • Statistical Science,
  • 2012
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL