Results 11 to 20 of about 192,768 (193)
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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Nonlinear shrinkage estimation of large-dimensional covariance matrices [PDF]
The Annals of Statistics, 2011 Many statistical applications require an estimate of a covariance matrix and/or its inverse. When the matrix dimension is large compared to the sample size, which happens frequently, the sample covariance matrix is known to perform poorly and may suffer ...
Ledoit, Olivier, Wolf, Michael
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Group Lasso estimation of high-dimensional covariance matrices [PDF]
Journal of Machine Learning Research, 2010 In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensional setting under the assumption that the process has a ...
Alvarez, Lilian Muniz +3 more
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Bounds for estimation of covariance matrices from heterogeneous samples [PDF]
IEEE Transactions on Signal Processing, 2008 This correspondence derives lower bounds on the mean-square error (MSE) for the estimation of a covariance matrix mbi Mp, using samples mbi Zk,k=1,...,K, whose covariance matrices mbi Mk are randomly distributed around mbi Mp.
Besson, Olivier +2 more
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Remote Sensing, 2022 Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds.
Ronghua Yang +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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Distributed Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noises
IEEE Access, 2020 This paper is concerned with distributed fusion (DF) estimation problem for nonlinear multi-sensor systems with correlated noises. Based on a recursive linear minimum variance estimation (RLMVE) framework, a novel filter is developed.
Gang Hao, Shuli Sun
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A finite-difference method for linearization in nonlinear estimation algorithms [PDF]
Modeling, Identification and Control, 1998 Linearizations of nonlinear functions that are based on Jacobian matrices often cannot be applied in practical applications of nonlinear estimation techniques. An alternative linearization method is presented in this paper.
Tor S. Schei
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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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