The Experts below are selected from a list of 5421 Experts worldwide ranked by ideXlab platform
Emil Karlsson - One of the best experts on this subject based on the ideXlab platform.
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Examensarbete Explicit Estimators for a Banded Covariance Matrix in a Multivariate Normal Distribution Explicit Estimators for a Banded Covariance Matrix in a Multivariate Normal Distribution
2020Co-Authors: Emil KarlssonAbstract:Abstract The problem of estimating mean and covariances of a multivariate normal Distributed Random Vector has been studied in many forms. This thesis focuses on the estimators proposed i
W K Theumann - One of the best experts on this subject based on the ideXlab platform.
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mean field theory of the ising Random anisotropy axis model in the large component limit
Physical Review B, 1993Co-Authors: David Dominguez, W K TheumannAbstract:The ising Random-anisotropy-axis model with additional noncubic anisotropy is investigated in mean-field theory in the limit p\ensuremath{\rightarrow}\ensuremath{\infty} for p-component Random Vectors on a lattice of N sites. The effects of anisotropy for statistically independent and identically Distributed Random-Vector components with a trimodal probability distribution are studied in the limits \ensuremath{\alpha}\ensuremath{\equiv}p/N=0 and \ensuremath{\alpha}g0. Ferromagnetic, mixed, and residual ordered phases are found in the first case, while only mixed ordered and spin-glass phases are found for the latter. Phase diagrams with explicit phase boundaries are obtained.
Dean M Young - One of the best experts on this subject based on the ideXlab platform.
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characterizations of noncentral chi squared generating covariance structures for a normally Distributed Random Vector
Sankhya A, 2016Co-Authors: Phil D Young, Dean M YoungAbstract:Let \({\mathbf {y}} \sim N_{n}\left ({\boldsymbol {\mu }}, {\mathbf {V}} \right )\), where y is a n×1 Random Vector and V is a n×n covariance matrix. We explicitly characterize the general form of the covariance structure V for which the family of quadratic forms \(\left \{{\mathbf {y}}^{\prime } {\mathbf {A}}_{i}{\mathbf {y}} \right \}^{k}_{i=1}\) for \(i \in \left \{1,...,k \right \}\), 2≤k≤n, is Distributed as multiples of mutually independent non-central chi-squared Random variables. We consider the case when the Ai’s and V are both nonnegative definite, including several cases where the Ai’s have special properties, and the case where the Ai’s are symmetric and V is positive definite. Our results generalize the work of Pavur (Sankhyā 51, 382–389, 1989), Baldessari (Comm. Statist. - Theory Meth. 16, 785–803, 1987), and Chaganty and Vaish (Linear Algebra Appl. 264, 421–437, 1997).
David Dominguez - One of the best experts on this subject based on the ideXlab platform.
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mean field theory of the ising Random anisotropy axis model in the large component limit
Physical Review B, 1993Co-Authors: David Dominguez, W K TheumannAbstract:The ising Random-anisotropy-axis model with additional noncubic anisotropy is investigated in mean-field theory in the limit p\ensuremath{\rightarrow}\ensuremath{\infty} for p-component Random Vectors on a lattice of N sites. The effects of anisotropy for statistically independent and identically Distributed Random-Vector components with a trimodal probability distribution are studied in the limits \ensuremath{\alpha}\ensuremath{\equiv}p/N=0 and \ensuremath{\alpha}g0. Ferromagnetic, mixed, and residual ordered phases are found in the first case, while only mixed ordered and spin-glass phases are found for the latter. Phase diagrams with explicit phase boundaries are obtained.
Phil D Young - One of the best experts on this subject based on the ideXlab platform.
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characterizations of noncentral chi squared generating covariance structures for a normally Distributed Random Vector
Sankhya A, 2016Co-Authors: Phil D Young, Dean M YoungAbstract:Let \({\mathbf {y}} \sim N_{n}\left ({\boldsymbol {\mu }}, {\mathbf {V}} \right )\), where y is a n×1 Random Vector and V is a n×n covariance matrix. We explicitly characterize the general form of the covariance structure V for which the family of quadratic forms \(\left \{{\mathbf {y}}^{\prime } {\mathbf {A}}_{i}{\mathbf {y}} \right \}^{k}_{i=1}\) for \(i \in \left \{1,...,k \right \}\), 2≤k≤n, is Distributed as multiples of mutually independent non-central chi-squared Random variables. We consider the case when the Ai’s and V are both nonnegative definite, including several cases where the Ai’s have special properties, and the case where the Ai’s are symmetric and V is positive definite. Our results generalize the work of Pavur (Sankhyā 51, 382–389, 1989), Baldessari (Comm. Statist. - Theory Meth. 16, 785–803, 1987), and Chaganty and Vaish (Linear Algebra Appl. 264, 421–437, 1997).