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Review on statistical methods for gene network reconstruction using expression data
Network modeling has proven to be a fundamental tool in analyzing the inner workings of a
cell. It has revolutionized our understanding of biological processes and made significant …
cell. It has revolutionized our understanding of biological processes and made significant …
Graph learning from data under Laplacian and structural constraints
Graphs are fundamental mathematical structures used in various fields to represent data,
signals, and processes. In this paper, we propose a novel framework for learning/estimating …
signals, and processes. In this paper, we propose a novel framework for learning/estimating …
Springer series in statistics
The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
Regularized rank-based estimation of high-dimensional nonparanormal graphical models
Regularized rank-based estimation of high-dimensional nonparanormal graphical models Page
1 The Annals of Statistics 2012, Vol. 40, No. 5, 2541–2571 DOI: 10.1214/12-AOS1041 © Institute …
1 The Annals of Statistics 2012, Vol. 40, No. 5, 2541–2571 DOI: 10.1214/12-AOS1041 © Institute …
Reconstruction from anisotropic random measurements
Random matrices are widely used in sparse recovery problems, and the relevant properties
of matrices with iid entries are well understood. The current paper discusses the recently …
of matrices with iid entries are well understood. The current paper discusses the recently …
Exact covariance thresholding into connected components for large-scale graphical lasso
We consider the sparse inverse covariance regularization problem or graphical lasso with
regularization parameter λ. Suppose the sample covariance graph formed by thresholding …
regularization parameter λ. Suppose the sample covariance graph formed by thresholding …
Gemini: Graph estimation with matrix variate normal instances
S Zhou - 2014 - projecteuclid.org
Gemini: Graph estimation with matrix variate normal instances Page 1 The Annals of Statistics
2014, Vol. 42, No. 2, 532–562 DOI: 10.1214/13-AOS1187 © Institute of Mathematical Statistics …
2014, Vol. 42, No. 2, 532–562 DOI: 10.1214/13-AOS1187 © Institute of Mathematical Statistics …
[HTML][HTML] Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov random fields
Consider a random vector with finite second moments. If its precision matrix is an M-matrix,
then all partial correlations are non-negative. If that random vector is additionally Gaussian …
then all partial correlations are non-negative. If that random vector is additionally Gaussian …
Graph learning from filtered signals: Graph system and diffusion kernel identification
This paper introduces a novel graph signal processing framework for building graph-based
models from classes of filtered signals. In our framework, graph-based modeling is …
models from classes of filtered signals. In our framework, graph-based modeling is …
Estimation of graphical models: An overview of selected topics
LP Chen - International Statistical Review, 2024 - Wiley Online Library
Graphical modelling is an important branch of statistics that has been successfully applied in
biology, social science, causal inference and so on. Graphical models illuminate …
biology, social science, causal inference and so on. Graphical models illuminate …