[PDF] Top 20 On the use of Empirical Likelihood for non-Gaussian clutter covariance matrix estimation
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On the use of Empirical Likelihood for non-Gaussian clutter covariance matrix estimation
... improved estimation scheme when the clutter distribution is ...unknown. The Empirical Like- lihood (EL) is a recent semi-parametric estimation method [11] which allows to ... Voir le document complet
6
Regularized Covariance Matrix Estimation in Complex Elliptically Symmetric Distributions Using the Expected Likelihood Approach - Part 2: The Under-Sampled Case
... this likelihood ratio for under-sampled training conditions in the case of complex ACG distributed ...data. For Gaussian distributed data, the under-sampled scenario has ... Voir le document complet
12
Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise
... suggests the use of a Gibbs-sampler [20], [24], whose procedure is reported in Table I, where N bi stands for the number of burn-in iterations and N r is the number ... Voir le document complet
7
Iterative Marginal Maximum Likelihood DOD and DOA Estimation for MIMO Radar in the Presence of SIRP Clutter
... by the Direction G´en´erale de l’Armement (D.G.A.) as well as the ANR ASTRID referenced ...ANR-17-ASTR-0015. the radar context [5, 6]. The latter is a two-scale, compound Gaussian ... Voir le document complet
8
Regularized Covariance Matrix Estimation in Complex Elliptically Symmetric Distributions Using the Expected Likelihood Approach - Part 1: The Over-Sampled Case
... Maximum Likelihood Ratio—Part I: Application to Antenna Array Detec- tion-Estimation With Perfect Wavefront Coherence,” IEEE ...strated, for multivariate complex Gaussian distribution, that ... Voir le document complet
13
Invariance properties of the likelihood ratio for covariance matrix estimation in some complex elliptically contoured distributions
... examine the p.d.f. of the likelihood ratio for two classes of complex ECD not covered in [ 1 ], namely X ∼ E SS M , T ( 0 , R , φ) and X ∼ E V S M , T ( 0 , R , φ) .... ... Voir le document complet
11
Knowledge-aided Bayesian covariance matrix estimation in compound-Gaussian clutter
... work of O. Besson was supported by the Mission pour la Recherche et l’Innovation Scientifique (MRIS) of the ...DGA. clutter returns are modeled as z k = √ τ k g k where g k is a Gaus- ... Voir le document complet
5
Estimation accuracy of non-standard maximum likelihood estimators
... deterministic estimation problems, the probability density function ...from the marginalization of a joint ...maximum likelihood estimators (MLEs) or any standard lower bound on their ... Voir le document complet
6
Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions
... computing the Wasserstein distance is expensive as it requires to minimize a cost function taking the form of an integral over the space of probability ...proposed, the latter is ... Voir le document complet
7
Contributions to probabilistic non-negative matrix factorization - Maximum marginal likelihood estimation and Markovian temporal models
... matrices non-négatives (NMF, de l’anglais non-negative matrix fac- torization) est aujourd’hui l’une des techniques de réduction de la dimensionnalité les plus répandues, dont les domaines ... Voir le document complet
164
Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime
... involving the eigenvalues of C 1 −1 C 2 are estimated from the empirical matrix ˆ C 1 −1 C ˆ 2 , the constraint n 1 > p is in- evitable to ensure the existence ... Voir le document complet
6
On the Efficient Use of the Informational Content of Estimating Equations: Implied Probabilities and Euclidean Empirical Likelihood
... on the n observed values X j , j = 1, · · · n given the possible conditioning values Z i , i = 1, · · · n, in case ...(1993), the distance between observed empirical distribution and an ... Voir le document complet
56
Recursive Estimation of State-Space Noise Covariance Matrix by Approximate Variational Bayes
... estimate the observation noise covariance matrix in a Kalman ...filter. The covariance matrix is assumed diagonal and the prior used is a product of inverse gamma ... Voir le document complet
10
Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
... establish the microergodicity or non-microergodicity of covariance parameters, and to pro- vide asymptotic results for estimators of microergodic ...particular covariance ... Voir le document complet
31
Regenerative block empirical likelihood for Markov chains
... 200x) Empirical likelihood is a powerful semi-parametric method increasingly inves- tigated in the ...In the case of dependent data, the classical empirical ... Voir le document complet
29
Gaussian fluctuations for linear spectral statistics of large random covariance matrices
... theorems for the characteristic roots of a sample covariance ...Random matrix theory and its applications, volume 18 of Lecture Notes ...Institute for Mathematical ...from ... Voir le document complet
53
Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments
... . The term computer experiment means that the use of the computer model f mod , for obtaining the simulated value of a given phenomenon of interest at x, ... Voir le document complet
254
Consistency of likelihood estimation for Gibbs point processes
... consistency, the next natural question concerns the asymptotic distribution of the ...in the present paper. Nonetheless, let us briefly mention the state of the art ... Voir le document complet
32
An M-Estimator for Robust Centroid Estimation on the Manifold of Covariance Matrices
... Recently, covariance matrices have been modeled as realiza- tions of Riemannian Gaussian distributions (RGDs) and used in classification algorithms such as k-means or Expectation- Maximization (EM) ... Voir le document complet
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On the convergence of the extremal eigenvalues of empirical covariance matrices with dependence
... sample of a centered random vector with unit covariance ...scaling, the smallest and the largest eigenvalues of the empirical covariance matrix converge, ... Voir le document complet
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