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Covariance regularization by thresholding
This paper considers regularizing a covariance matrix of p variables estimated from n observations, by hard thresholding. We show that the thresholded estimate is consistent in the operator norm asExpand
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Regularized estimation of large covariance matrices
This paper considers estimating a covariance matrix of p variables from n observations by either banding the sample covariance matrix or estimating a banded version of the inverse of the covariance.Expand
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Sparse permutation invariant covariance estimation
The paper proposes a method for constructing a sparse estima- tor for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihoodExpand
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Some theory for Fisher''s linear discriminant function
Introducing small amounts of cobalt, nickel, or iron into a weld joint between members of aluminum and its alloys substantially eliminates weld porosity. These additives may be included in the rod orExpand
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Maximum Likelihood Estimation of Intrinsic Dimension
TLDR
We propose a new method for estimating intrinsic dimension of a dataset derived by applying the principle of maximum likelihood to the distances between close neighbors. Expand
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Generalized Thresholding of Large Covariance Matrices
We propose a new class of generalized thresholding operators that combine thresholding with shrinkage, and study generalized thresholding of the sample covariance matrix in high dimensions.Expand
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Sure independence screening for ultrahigh dimensional feature space Discussion
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Sparse Multivariate Regression With Covariance Estimation
TLDR
We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. Expand
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Consistency of community detection in networks under degree-corrected stochastic block models
TLDR
We establish general theory for checking consistency of community detection under the degree-corrected stochastic block model and compare several community detection criteria under both the standard and the degree corrected models, as well as compare their relative performance in practice. Expand
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Joint estimation of multiple graphical models.
Gaussian graphical models explore dependence relationships between random variables, through the estimation of the corresponding inverse covariance matrices. In this paper we develop an estimator forExpand
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