Sparse Permutation Invariant Covariance Estimation: Motivation ...

Recommend Documents

Sparse multivariate regression with covariance estimation

May 18, 2010 - Citigroup. 0.65(0.17) 0.61(0.13) 0.66(0.14) 0.62(0.13) 0.62(0.13) 0.59. IBM. 0.62(0.14) 0.49(0.10) 0.49(0.10) 0.49(0.10) 0.47(0.09) 0.51. AIG.

Sparse inverse covariance estimation with the lasso

Aug 27, 2007 - Abstract. We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate.

Dynamic covariance estimation using sparse ...

tional Workshop on Statistical Modelling, Johannes Kepler UniversitÃ¤t Linz, 6â10. July 2015. The copyright ... The top panel of Figure 1 exemplifies the output of the sampler for the first ... the R package stochvol. Journal of Statistical Softwar

SPARSE COVARIANCE ESTIMATION UNDER ... - Semantic Scholar

and Alfred O. Hero III. â,â . University of Michigan, âEECS ..... [8] Roger A. Horn and Charles R. Johnson, Matrix Analysis,. Cambridge University Press, 1990.

Sparse Inverse Covariance Matrix Estimation Using Quadratic ...

The non-zero pattern of this inverse covariance matrix Σ−1 can ... ularized by the l1 norm of the entries (off-diagonal entries) of the inverse covariance matrix. The.

Sparse covariance matrix estimation in high ...

Oct 30, 2017 - Abstract: We study the estimation of the covariance matrix Î£ of a ... various novel methods of high-dimensional matrix estimation have been ...

Split Bregman Method for Sparse Inverse Covariance Estimation - arXiv

Dec 27, 2010 - School of Information and Computer Science, University of ... Department of Mathematics, University of California, Los Angeles, CA 90095, USA.

Scalable sparse covariance estimation via self ... - Semantic Scholar

May 13, 2014 - of our algorithm, both in terms of recovery efficiency and complexity. ..... USB. U.S. Bank Corp. SNY. Sanofi health. AURR. Aurora Russia. IPO.

Sparse multivariate regression with covariance estimation - Stat-UMN

May 18, 2010 - Sparse multivariate regression with covariance estimation. Adam J. Rothman, Elizaveta Levina, and Ji Zhu. Department of Statistics. University ...

Sparse estimation of large covariance matrices via a nested ... - arXiv

By Elizaveta Levina,1 Adam Rothman and Ji Zhu2. University of Michigan. The paper proposes a new covariance estimator for large covariance matrices when ...

doa estimation by covariance matrix sparse ... - IEEE Xplore

In this paper, we propose a direction-of-arrival estimation method by covariance matrix sparse reconstruction of co- prime array. Specifically, source locations ...

A Well-Conditioned and Sparse Estimation of Covariance and Inverse ...

Abstract. We develop a method for estimating well-conditioned and sparse covariance and inverse covariance matrices from a sample of vectors drawn from a ...

Permutation-invariant monotones for multipartite entanglement ...

Apr 22, 2008 - arXiv:0804.3461v1 [quant-ph] 22 Apr 2008. Permutation-invariant monotones for multipartite entanglement characterization. Xi-Jun Ren,â Wei ...

List permutation invariant linear codes: Theory and

the discovery of list permutation invariant codes whose upper bound coincides with ...... performed6 with the aid of âSciPyâ package in Python. 6Due to the ...

Sparse Inverse Covariance Selection via Alternating Linearization ...

Oct 30, 2010 - In this paper, we propose an alternating linearization method (ALM) for ... We show how to use ALM to solve SICS problems and give intuition ...

Sparse Inverse Covariance Selection via Alternating Linearization ...

Oct 30, 2010 - Lehigh University [email protected] ..... this constraint explicitly nor do so by a line search on Âµk, since in practice this degrades the perfor-.

OPTIMAL RATES OF CONVERGENCE FOR SPARSE COVARIANCE ...

This paper considers estimation of sparse covariance matrices and establishes the optimal rate of convergence under a range of matrix operator norm and ...

Sparse Bayesian Time-Varying Covariance ...

Nov 22, 2017 - factor models and show excellent performance in terms of posterior rates of ... Extensive simulation studies are presented in Section 4, where ...... (Fama and French 1993); the momentum factor MOM (Carhart 1997) captures ...

COVARIANCE MATRIX ESTIMATION FOR THE

error), and the task is to fill in the missing entries. If we let Xs be the columns ... A high profile example of recommender systems is the Netflix prize problem [6]. 2 ...

Sparse Signal Estimation by Maximally Sparse Convex

Jan 3, 2014 - formulations of the sparse signal estimation problem. In the non-convex case, generally only a local optimal solution can be ensured; hence ... of near singular systems. ...... the MATLAB software package 'SeDuMi' [64]. Often ...

Consistent Covariance Matrix Estimation with

promotes the discovery and use of these resources. For more ... elrors. In this paper we present conditions under which a simple extension .... 1998 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. [ 549

TRANSFORMATION INVARIANT SPARSE ... - Mikkel N. Schmidt

e-mail: 1mm,[email protected] ... The visual area 1 (V1) in the visual cortex of the human brain is retinotopi- ... invariant to the position of the visual stimulus. Hence, these fea- ..... selected image patch, not already used in the dictionary, A. N

Sparse Channel Estimation for OFDM

Peng Chengâ â¤, Lin Guiâ â¤, Meixia Taoâ , Y Jay Guoâ¤ , Xiaojing Huangâ¤ and Yun Ruiâ¡. â Dept. of Electronic Engineering, Shanghai Jiao Tong University, ...

ANSIG â An Analytic Signature for Permutation-Invariant Two ...

2D shape, usually described by a set of 2D points. The chal- lenge of this representation is that it must not only capture the characteristics of the shape but also ...

Sparse Permutation Invariant Covariance Estimation: Motivation ...

Download PDF

4 downloads 23939 Views 486KB Size Report

Comment

[email protected]. April 19, 2012. David Prince (UW). SPICE. April 19, 2012. 1 / 11 .... Uses Naive Bayes, Ledoit-Wolf and SPICE with three ways of deriving.

Sparse Permutation Invariant Covariance Estimation: Motivation, Background and Key Results David Prince Biostat 572 [email protected]

April 19, 2012

David Prince (UW)

SPICE

April 19, 2012

1 / 11

Electronic Journal of Statistics Vol. 2 (2008) 494–515 ISSN: 1935-7524 DOI: 10.1214/08-EJS176

Sparse permutation invariant covariance estimation Adam J. Rothman University of Michigan Ann Arbor, MI 48109-1107 e-mail: [email protected]

Peter J. Bickel University of California Berkeley, CA 94720-3860 e-mail: [email protected]

Elizaveta Levina∗ University of Michigan Ann Arbor, MI 48109-1107 e-mail: [email protected]

Ji Zhu University of Michigan Ann Arbor, MI 48109-1107 e-mail: [email protected]

David Prince (UW)

Abstract: The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihood approach and forces sparsity by using a lasso-type penalty. We establish a rate of conSPICE vergence in the Frobenius norm as both data dimension p and sample size

April 19, 2012

2 / 11

Motivation

Reductionism vs. Dynamism Gene-gene interaction networks High-dimensional setting, i.e. n