Results 41 to 50 of about 285,691 (282)
A data driven equivariant approach to constrained Gaussian mixture modeling
, 2016 Maximum likelihood estimation of Gaussian mixture models with different class-specific covariance matrices is known to be problematic. This is due to the unboundedness of the likelihood, together with the presence of spurious maximizers. Existing methods
Di Mari, Roberto +2 more
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Two sample tests for high-dimensional covariance matrices [PDF]
, 2012 We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance--covariance matrices, and the other is on off-diagonal sub-matrices, which define the covariance between two ...
Chen, Song Xi, Li, Jun
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Proportionality of Covariance Matrices
The Annals of Statistics, 1987 S\({}_ 0,S_ 1,...,S_ k\) are mutually independent p by p matrices, \(S_ i\) having a Wishart distribution with \(n_ i\) degrees of freedom and expectation \(\Sigma_ i\). The likelihood ratio test of the hypothesis \(\Sigma_ i=\lambda_ i\Sigma_ 0\) for \(i=1,...,k\) is developed.
openaire +2 more sources
bspcov: An R Package for Bayesian sparse covariance matrix estimation
SoftwareXThe bspcov R package provides a Bayesian inference for covariance matrices. The bspcov is developed to aid in research that involves estimating constrained covariance matrices by enabling the use of state-of-the-art Bayesian inference methods.
Kyeongwon Lee +3 more
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Penalized maximum likelihood for multivariate Gaussian mixture
, 2001 In this paper, we first consider the parameter estimation of a multivariate random process distribution using multivariate Gaussian mixture law. The labels of the mixture are allowed to have a general probability law which gives the possibility to ...
Mohammad-Djafari, Ali, Snoussi, Hichem
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Covariance Estimation in High Dimensions via Kronecker Product Expansions [PDF]
, 2013 This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d.
Alfred O. Hero Iii +2 more
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Universality of covariance matrices
The Annals of Applied Probability, 2014 In this paper we prove the universality of covariance matrices of the form $H_{N\times N}={X}^{\dagger}X$ where $X$ is an ${M\times N}$ rectangular matrix with independent real valued entries $x_{ij}$ satisfying $\mathbb{E}x_{ij}=0$ and $\mathbb{E}x^2_{ij}={\frac{1}{M}}$, $N$, $M\to \infty$.
Pillai, Natesh S., Yin, Jun
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Covariance Matrices under Bell-like Detections [PDF]
Open Systems & Information Dynamics, 2013 We derive a simple formula for the transformation of an arbitrary covariance matrix of (n + 2) bosonic modes under general Bell-like detections, where the last two modes are combined in an arbitrary beam splitter (i.e., with arbitrary transmissivity) and then homodyned.
Spedalieri, Gaetana +2 more
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore how cerebral hypoxia and Normal‐Appearing White Matter (NAWM) integrity affect MS lesion burden and clinical course. Methods Seventy‐nine MS patients, including 13 clinically isolated syndrome (CIS) patients and 66 relapsing–remitting multiple sclerosis (RRMS) patients, and 44 healthy controls (HCs) were recruited from ...
Xinli Wang +8 more
wiley +1 more source
Genetics and Molecular Biology, 2007 Comparisons of covariance patterns are becoming more common as interest in the evolution of relationships between traits and in the evolutionary phenotypic diversification of clades have grown. We present parallel analyses of covariance matrix similarity
James M. Cheverud, Gabriel Marroig
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