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Published in 2017 at "Wiley Interdisciplinary Reviews: Computational Statistics"
DOI: 10.1002/wics.1415
Abstract: Covariance matrix and its inverse, known as the precision matrix, have many applications in multivariate analysis because their elements can exhibit the variance, correlation, covariance, and conditional independence between variables. The practice of estimating the…
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Keywords:
biology;
network;
precision matrix;
covariance ... See more keywords
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Published in 2019 at "Acta Mathematica Sinica, English Series"
DOI: 10.1007/s10114-019-7326-8
Abstract: In this paper, we consider the estimation of a high dimensional precision matrix of Gaussian graphical model. Based on the re-parameterized likelihood, we obtain the full conditional distribution of all parameters in Cholesky factor. Furthermore,…
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Keywords:
large precision;
bayesian estimation;
precision matrix;
precision ... See more keywords
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Published in 2018 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2018.03.020
Abstract: We consider the statistical inference for high-dimensional precision matrices. Specifically, we propose a data-driven procedure for constructing a class of simultaneous confidence regions for a subset of the entries of a large precision matrix. The…
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Keywords:
entries large;
confidence regions;
precision;
precision matrix ... See more keywords
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Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac178
Abstract: MOTIVATION Transcriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying Gene Regulatory Network (GRN), and reliably inferring such…
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Keywords:
gene regulatory;
precision matrix;
matrix estimation;
robust precision ... See more keywords
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Published in 2018 at "Monthly Notices of the Royal Astronomical Society"
DOI: 10.1093/mnras/stx2566
Abstract: Computing the inverse covariance matrix (or precision matrix) of large data vectors is crucial in weak lensing (and multiprobe) analyses of the large-scale structure of the Universe. Analytically computed covariances are noise-free and hence straightforward…
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Keywords:
numerical simulations;
cosmological parameters;
precision matrix;
covariance ... See more keywords
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Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3126675
Abstract: We consider the problem of learning a sparse undirected graph underlying a given set of multivariate data. We focus on graph Laplacian-related constraints on the sparse precision matrix that encodes conditional dependence between the random…
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Keywords:
related constraints;
precision matrix;
laplacian related;
graph ... See more keywords
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Published in 2020 at "Applied Sciences"
DOI: 10.3390/app10186571
Abstract: In the field of speaker verification, probabilistic linear discriminant analysis (PLDA) is the dominant method for back-end scoring. To estimate the PLDA model, the between-class covariance and within-class precision matrices must be estimated from samples.…
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Keywords:
within class;
precision matrix;
class precision;
speaker verification ... See more keywords