Results 21 to 30 of about 9,759 (265)
Covariance Matrix Estimation With Heterogeneous Samples
IEEE Transactions on Signal Processing, 2008 We consider the problem of estimating the covariance matrix Mp of an observation vector, using heterogeneous training samples, i.e., samples whose covariance matrices are not exactly Mp. More precisely, we assume that the training samples can be clustered into K groups, each one containing Lk, snapshots sharing the same covariance matrix Mk ...
Olivier Besson +2 more
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A Compound Decision Approach to Covariance Matrix Estimation
Biometrics, 2022 AbstractCovariance matrix estimation is a fundamental statistical task in many applications, but the sample covariance matrix is suboptimal when the sample size is comparable to or less than the number of features. Such high-dimensional settings are common in modern genomics, where covariance matrix estimation is frequently employed as a method for ...
Huiqin Xin, Sihai Dave Zhao
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A subspace method for array covariance matrix estimation [PDF]
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016 This paper introduces a subspace method for the estimation of an array covariance matrix. It is shown that when the received signals are uncorrelated, the true array covariance matrices lie in a specific subspace whose dimension is typically much smaller than the dimension of the full space.
Rahmani, Mostafa, Atia, George K.
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Maximum Likelihood Estimation for the Mixed Weibull-Exponential Models in the Case of Type I Censored Samples [PDF]
The Egyptian Statistical Journal, 1983 In life testing (type I censored), a new statistical model called mixed Weibull-exponential distribution has been suggested; the maximum likelihood estimates for its unknown parameters and their approximate asymptotic, variance covariance matrix have ...
S. K. Ashour
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Random matrix-improved estimation of covariance matrix distances [PDF]
Journal of Multivariate Analysis, 2019 Given two sets $x_1^{(1)},\ldots,x_{n_1}^{(1)}$ and $x_1^{(2)},\ldots,x_{n_2}^{(2)}\in\mathbb{R}^p$ (or $\mathbb{C}^p$) of random vectors with zero mean and positive definite covariance matrices $C_1$ and $C_2\in\mathbb{R}^{p\times p}$ (or $\mathbb{C}^{p\times p}$), respectively, this article provides novel estimators for a wide range of distances ...
Couillet, Romain +3 more
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Matrix rank and inertia formulas in the analysis of general linear models
Open Mathematics, 2017 Matrix mathematics provides a powerful tool set for addressing statistical problems, in particular, the theory of matrix ranks and inertias has been developed as effective methodology of simplifying various complicated matrix expressions, and ...
Tian Yongge
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IEEE Access, 2018 This paper addresses the estimation of large-dimensional covariance matrices under both normal and nonnormal distributions. The shrinkage estimators are constructed by convexly combining the sample covariance matrix and a structured target matrix.
Jianbo Li, Jie Zhou, Bin Zhang
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New properties for Tyler's covariance matrix estimator [PDF]
2016 50th Asilomar Conference on Signals, Systems and Computers, 2016 In this paper, we deal with covariance matrix estimation in complex elliptically symmetric (CES) distributions. We focus on Tyler's estimator (TyE) and the well-known sample covariance matrix (SCM). TyE is widely used in practice, but its statistical behavior is still poorly understood.
Draskovic, Gordana, Pascal, Frédéric
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GMM Estimators for Binary Spatial Models in R
Journal of Statistical Software, 2023 Despite the huge availability of software to estimate cross-sectional spatial models, there are only few functions to estimate models dealing with spatial limited dependent variable. This paper fills this gap introducing the new R package spldv.
Gianfranco Piras, Mauricio Sarrias
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High‐dimensional covariance matrix estimation [PDF]
WIREs Computational Statistics, 2019 AbstractCovariance matrix estimation plays an important role in statistical analysis in many fields, including (but not limited to) portfolio allocation and risk management in finance, graphical modeling, and clustering for genes discovery in bioinformatics, Kalman filtering and factor analysis in economics. In this paper, we give a selective review of
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