A note on conditional covariance matrices for elliptical distributions
, 2017 In this short note we provide an analytical formula for the conditional covariance matrices of the elliptically distributed random vectors, when the conditioning is based on the values of any linear combination of the marginal random variables.
Jaworski, Piotr, Pitera, Marcin
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Massive data compression for parameter-dependent covariance matrices [PDF]
, 2017 We show how the massive data compression algorithm MOPED can be used to reduce, by orders of magnitude, the number of simulated datasets that are required to estimate the covariance matrix required for the analysis of gaussian-distributed data.
A. Heavens +3 more
semanticscholar +1 more source
A necessary and sufficient condition for edge universality at the largest singular values of covariance matrices [PDF]
The Annals of Applied Probability, 2016 In this paper, we prove a necessary and sufficient condition for the edge universality of sample covariance matrices with general population. We consider sample covariance matrices of the form $\mathcal Q = TX(TX)^{*}$, where the sample $X$ is an $M_2 ...
Xiucai Ding, Fan Yang
semanticscholar +1 more source
A Synovium‐on‐Chip Platform to Study Multicellular Interactions in Arthritis
Advanced Healthcare Materials, EarlyView.The Synovium‐on‐Chip comprises a thin microporous PDMS membrane to support co‐culture of fibroblast‐like synoviocytes (FLS), THP‐1‐derived macrophages, and endothelial cells, enabling real‐time analysis of synovial‐vascular interactions. FLS migration through the pores drives endothelial remodeling, while TNF‐α stimulation induces robust inflammatory ...
Laurens R. Spoelstra +8 more
wiley +1 more source
On the Significance of Covariance for Constraining Theoretical Models from Galaxy Observables
The Astrophysical JournalIn this study, we investigate the impact of covariance within uncertainties on the inference of cosmological and astrophysical parameters, specifically focusing on galaxy stellar mass functions derived from the CAMELS simulation suite.
Yongseok Jo +3 more
doaj +1 more source
Positive Definite $\ell_1$ Penalized Estimation of Large Covariance Matrices [PDF]
, 2012 The thresholding covariance estimator has nice asymptotic properties for estimating sparse large covariance matrices, but it often has negative eigenvalues when used in real data analysis.
Ma, Shiqian, Xue, Lingzhou, Zou, Hui
core
Regularized estimation of large covariance matrices [PDF]
, 2008 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.
P. Bickel, E. Levina
semanticscholar +1 more source
Gaussian Distributions on Riemannian Symmetric Spaces: Statistical Learning With Structured Covariance Matrices [PDF]
IEEE Transactions on Information Theory, 2016 The Riemannian geometry of covariance matrices has been essential to several successful applications, in computer vision, biomedical signal and image processing, and radar data processing.
S. Said, H. Hajri, L. Bombrun, B. Vemuri
semanticscholar +1 more source
Advanced Materials, EarlyView.Detecting proteins secreted by a single cell while retaining its viability remains challenging. A particles‐in‐particle (PiPs) system made by co‐encapsulating barcoded microparticles (BMPs) with a single cell inside an alginate hydrogel particle is introduced.
Félix Lussier +10 more
wiley +1 more source
Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes [PDF]
, 2013 Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter.
Bachoc, François
core +3 more sources