Results 11 to 20 of about 192,869 (306)
Efficient Distributed Estimation of Inverse Covariance Matrices [PDF]
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016 In distributed systems, communication is a major concern due to issues such as its vulnerability or efficiency. In this paper, we are interested in estimating sparse inverse covariance matrices when samples are distributed into different machines.
Arroyo, Jesús, Hou, Elizabeth
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Estimation of functionals of sparse covariance matrices
The Annals of Statistics, 2015 High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other $\ell_r$ norms.
Fan, Jianqing +2 more
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ESTIMATION OF TIME-VARYING COVARIANCE MATRICES FOR LARGE DATASETS [PDF]
SSRN Electronic Journal, 2021 Time variation is a fundamental problem in statistical and econometric analysis of macroeconomic and financial data. Recently, there has been considerable focus on developing econometric modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or nonparametric kernel methods.
Yiannis Dendramis +2 more
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Robust estimation of high-dimensional covariance and precision matrices [PDF]
Biometrika, 2018 High-dimensional data are often most plausibly generated from distributions with complex structure and leptokurtosis in some or all components. Covariance and precision matrices provide a useful summary of such structure, yet the performance of popular matrix estimators typically hinges upon a sub-Gaussianity assumption.
Marco Avella-Medina +3 more
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Partial estimation of covariance matrices [PDF]
Probability Theory and Related Fields, 2011 A classical approach to accurately estimating the covariance matrix of a p-variate normal distribution is to draw a sample of size n > p and form a sample covariance matrix. However, many modern applications operate with much smaller sample sizes, thus calling for estimation guarantees in the regime n << p.
Levina, Elizaveta, Vershynin, Roman
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Robust Estimation of Covariance Matrices: Adversarial Contamination and Beyond [PDF]
Statistica Sinica, 2022 We consider the problem of estimating the covariance structure of a random vector $Y\in \mathbb R^d$ from a sample $Y_1,\ldots,Y_n$. We are interested in the situation when $d$ is large compared to $n$ but the covariance matrix $Σ$ of interest has (exactly or approximately) low rank.
Stanislav Minsker, Lang Wang
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Robust shrinkage estimation of high-dimensional covariance matrices [PDF]
2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, 2010 We address high dimensional covariance estimation for elliptical distributed samples, which are also known as spherically invariant random vectors (SIRV) or compound-Gaussian processes. Specifically we consider shrinkage methods that are suitable for high dimensional problems with a small number of samples (large $p$ small $n$).
Chen, Yilun +2 more
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On the Properties of Estimates of Monotonic Mean Vectors for Multivariate Normal Distributions [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 .Problems concerning estimation of parameters and determination the statistic, when it is known a priori that some of these parameters are subject to certain order restrictions, are of considerable interest.
Abouzar Bazyari
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IEEE Access, 2022 Covariance matrices of spatially-correlated wireless channels in millimeter wave (mmWave) vehicular networks can be employed to design environment-aware beamforming codebooks.
Imtiaz Nasim, Ahmed S. Ibrahim
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Sensors, 2020 Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space ...
Chenghao Shan +3 more
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