T. Sugiyama, Distributions of the largest latent root of the multivariate complex Gaussian distribution, Ann. Inst. Statist. Math., 24 (1972), pp. 87-94.Sugiyama, T. (1972). Distributions of the largest latent root of the multivariate complex Gaussian distribution, Ann. Inst. Statist. Math. , 24 , 87–94.
In this paper, asymptotic expansions of the non-null distribution of the Wilks' statistic for Manova based on the complex multivariate Gaussian distribution is obtained for the cases (a) when the non-centrality parameter ω∼is constant and (b) when ∼Is of the same order as the sample ...
Statistical analysis based on a certain multivariate complex Gaussian distribution Annals of Mathematical Statistics, 34 (1963), pp. 152-177 CrossrefGoogle Scholar [6] N.R. Goodman Distribution of determinant of a complex Wishart distributed matrix Annals of Mathematical Statistics, 34 (1963), pp....
box 4 in Fig.1) is one of the key problems in machine learning and a number of methodologies have been proposed for this task, such as regression trees37or Support Vector Machines38. Multivariate Gaussian distributions provide a straightforward approach to this problem allowing to estimate the im...
"Moments of the complex multivariate normal distribution," Linear algebra and its applications, vol. 237/238, pp. 191- 204, 1996.Sultan S. A., Tracy D. S., "Moments of the complex multivariate normal distribution," Linear Algebra and its Applications, vol. 237/238, pp. 191-204, 1996...
The problem of estimating a mean matrix of a multivariate complex normal distribution with an unknown covariance matrix is considered under an invariant loss function. By using complex versions of the Stein identity, the Stein-Haff identity, and calculus on eigenvalues, a formula is obtained for ...
(population of bots characterized by different layouts) from a multivariate Gaussian distribution\({\mathcal{N}}\). While there is no mathematical proof of convergence to global optimum, CMA-ES has proven reliable in dealing with multi-modal, low-dimensional continuous problems69,70and has been ...
Two assumptions are made in this study. First, we assume that the functional data follows a joint multivariate Gaussian distribution, which is commonly used in the traditional literature on probability graphical models (Qiao et al., 2019) [36]. Additionally, we assume that the compositional data...
To quantify the effectiveness of network visualization, an area estimation method based on a multivariate Gaussian distribution with noise tolerance is introduced. A layout with a high metric prevents the visualization from entanglement while making as much full use of the canvas space as possible. ...
In PDTDFB domain, the PDTDFB coefficients are modeled as multivariate non-Gaussian distribution taking into account the interscale and intrascale dependency correlation. Then a multivariate shrinkage function is derived by the maximum a posterior (MAP) estimator and the denoised coefficients are ...