Results 31 to 40 of about 55,908 (306)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingAccurate error covariance is crucial for postprocessing gravity recovery and climate experiment (GRACE) gravity field solutions in terms of spherical harmonic coefficients (SHCs).
Lin Zhang +3 more
doaj +1 more source
Patch-Based Principal Covariance Discriminative Learning for Image Set Classification
IEEE Access, 2017 Image set classification has attracted increasing attention with respect to the use of significant amounts of within-set information. The covariance matrix is a natural and effective descriptor for describing image sets. Non-singular covariance matrices,
Hengliang Tan, Ying Gao
doaj +1 more source
Estimating Covariance Matrices
The Annals of Statistics, 1991 Let \(S_ 1\sim W_ p(\Sigma_ 1,n_ 1)\) and \(S_ 2\sim W_ p(\Sigma_ 2,n_ 2)\) be two independent \(p\times p\) Wishart matrices. It is desired to consider the minimax estimation of \((\Sigma_ 1,\Sigma_ 2)\) under the loss function \[ \sum_{i=1}^ 2\{\hbox {tr}(\Sigma_ i^{-1}\hat\Sigma_ i-\log| \Sigma_ i^{- 1}\hat\Sigma_ i|-p\}, \] extending known results ...
openaire +2 more sources
A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance
Sensors, 2018 The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system.
Binqi Zheng +3 more
doaj +1 more source
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
Covariance matrices and valuations
Advances in Applied Mathematics, 2013 The moment matrix of a function \(f\) of real variables \(x_1,\dots,x_n\) is the matrix, whose \((i,j)\)-th entry is the integral of \(x_i x_j f\) over \(\mathbb{R}^n\). The moment matrix can be regarded as a matrix-valued valuation on the space of functions with finite second moments: recall that a valuation on a space of functions \(L\) is a function
openaire +1 more source
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
doaj +1 more source
Journal of Mathematics, 2021 The explicit representation for the limiting spectral moments of sample covariance matrices generated by the periodic autoregressive model (PAR) is established.
Jin Zou, Dong Han
doaj +1 more source
Universal correlations and power-law tails in financial covariance matrices [PDF]
, 2010 Signatures of universality are detected by comparing individual eigenvalue distributions and level spacings from financial covariance matrices to random matrix predictions.
Akemann, G +5 more
core +1 more source
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