Haut PDF Regularized Covariance Matrix Estimation in Complex Elliptically Symmetric Distributions Using the Expected Likelihood Approach - Part 2: The Under-Sampled Case
Regularized Covariance Matrix Estimation in Complex Elliptically Symmetric Distributions Using the Expected Likelihood Approach - Part 2: The Under-Sampled Case
... independent complex Gaussian random vector ...of the p.d.f. of the texture is seldom available, the usual way is to treat the textures as unknown deterministic quantities and to carry ...
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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 ...multivariate complex Gaussian distribution, that the ...
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On the Expected Likelihood Approach for Assessment of Regularization Covariance Matrix
... Albeit the two mechanisms for generating are different, from a likelihood point of view the two representations are equivalent, as far as only assessment of from is ...course, in the ...
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Robust Markowitz mean-variance portfolio selection under ambiguous covariance matrix *
... In the above cited papers, the continuous-time Markowitz problem was essentially stu- died in the framework of a Black-Scholes model, and abundant research has been conducted to extend ...
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Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
... detection In order to illustrate the consequences of our results we shall present two ...examples. In the first one we are given data distributed according to the spiked Wigner model ...
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Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions
... probability distributions. The Wasserstein distance, initially inspired by Monge [1] and later by Kan- torovich [2] in a transport theory analogy, provides a natural notion of dissimilarity ...
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Exact Maximum Likelihood estimation for the BL-GARCH model under elliptical distributed innovations
... variance in non-linear time series is a challenging modelling exercise, con- sidered among many others things by Tong ...(1990). In particular, the stylized fact that the volatility of ...
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Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise
... with covariance matrix R R and the R k ’s are positive ...process. In this scenario, the problem of estimating the covariance matrix is generally ...sample ...
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A moment matrix approach to computing symmetric cubatures
... divided in three parts. With the help of the last algorithm in the previous section, we first provide a way to determine the existence for a given measure of cubatures of a given ...
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Joint Estimation of Location and Scatter in Complex Elliptical Distributions: A robust semiparametric and computationally efficient R-estimator of the shape matrix
... campaigns, the (Real or Complex) Elliptically Symmetric (ES) model has been recently adopted to characterize the statistical data ...behavior. The RES and CES models in ...
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On the Cramer Rao bound and maximum likelihood in passive time delay estimation for complex signals
... tributed as (x 1 (t), x 2 (t)) t ∈[−T/2,T/2] and circular if s(t) is circular. For real-valued x j (t), a well known theorem of statistics [7, Sec. 6.4] asserts that these Fourier coefficients ...
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Large N expansion of the 2-matrix model, multicut case.
... appearing in 2-matrix models and the associated Riemann-Hilbert problem”, preprint CRM-2852 (2002), Saclay T02/097, ...“Random Matrix Models and Their Applications”, MSRI Research ...
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Beef cattle methane emission estimation using the eddy covariance technique in combination with geolocation
... for the Spring 2014 campaign with each point corresponding to a 30-minute 315 measurement ...interval. The different regression lines correspond to the reduced major axis method (RMA), the ...
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Maximum likelihood estimation for Gaussian processes under inequality constraints
... Also, in practice, computing the cMLE requires a discretization of the constraints, for instance using a piecewise affine interpolation as in Section 5 , or a finite set of constrained ...
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Nonparametric estimation of the expected discounted penalty function in the compound Poisson model
... if the model selection procedures (18) for the Laguerre deconvolution estimators lead to the same performance than the naive choice m = b5T 1/10 ...results in Tables 1, 2 and 3. ...
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Controlling light in complex media beyond the acoustic diffraction-limit using the acousto-optic transmission matrix
... measure the AOTM 6 . To compensate for any slow phase drifts of the reference arm, a flat-phase mask was displayed on the SLM before each input mode was injected, and the phase of the ...
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Improved Estimation of the Distance between Covariance Matrices
... Terms— Covariance distance, random matrix the- ory, Fisher information ...between covariance matrices are objects of inter- est for many engineering applications, among which machine learning ...
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The L^2 –Alexander torsion is symmetric
... fQs 1 ; : : : ; Qs b ; cg defines an Euler structure K.e/ for N , which only depends on e. Put differently, we defined a map KW Eul.N; @N / ! Eul.N / which is easily seen to be H 1 .N /–equivariant. Given e 2 ...
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Cross-validation estimation of covariance parameters under fixed-domain asymptotics
... exponential covariance function. Under fixed-domain asymptotics, we prove the strong consistency and asymptotic normality of a cross validation estimator of the microergodic covariance ...
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Performance analysis of beamformers using generalized loading of the covariance matrix in the presence of random steering vector errors
... of the robust beamformer (4) in the case where and both and are ...Therefore, the analysis does not apply directly to [10]–[12], where the loading level depends on the ...
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