A note on maximizing a special concave function subject to simultaneous Loewner order constraints
TLDR
In this article, the authors show that for a special function, which is proportional to the density of a Wishart distribution, reparametrization can lead to maximization of a concave function.About:
This article is published in Linear Algebra and its Applications.The article was published on 1992-11-01 and is currently open access. It has received 5 citations till now. The article focuses on the topics: Concave function & Wishart distribution.read more
Citations
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Handbook of Semidefinite Programming
TL;DR: Conditions and an accurate semidefinite programming solver are described in The Journal of the SDPA family for solving large-scale SDPs and in Handbook on Semidefinitely Programming.
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Max-min eigenvalue problems, primal-dual Interior point algorithms, and Trust region subproblemst
TL;DR: Two Primal-dual interior point algorithms are presented for the problem of maximizing the smallest eigenvalue of a symmetric matrix over diagonal perturbations, and prove to be simple, robust, and efficient.
Semidefinite and Cone Programming Bibliography/Comments
TL;DR: This online technical report presents abstracts (short outlines) of papers related to semidefinite programming, grouped by subject.
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REML Estimation of Covariance Matrices with Restricted Parameter Spaces
TL;DR: In this paper, the authors extend these results to handle a wide class of restricted parameter spaces, and state the conditions required for a parameterization to be a member of the class, discuss the implementation of the results for several different classes of parameterization and discuss estimation with both balanced and unbalanced data.
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One-sided test of a covariance matrix with a known null value
TL;DR: In this paper, the authors assume a random sample of n + 1 from a distribution and study the test statistics for H 0 ∑ = ∑0 versus H 1 ∑ ≥ ∑ 0 and H 2 ∑ ≠ ∑
References
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Book
An Introduction to Multivariate Statistical Analysis
TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
Journal ArticleDOI
Introduction to Multivariate Statistical Analysis.
William G. Madow,T. W. Anderson +1 more
Book
Inequalities: Theory of Majorization and Its Applications
TL;DR: In this paper, Doubly Stochastic Matrices and Schur-Convex Functions are used to represent matrix functions in the context of matrix factorizations, compounds, direct products and M-matrices.
Journal ArticleDOI
Linear Statistical Inference and Its Applications.
Book
Order restricted statistical inference
TL;DR: In this paper, a set of multinomial parameters are derived about distributions subject to shape restrictions, and a conditional expectation given a sigma-lattice is given in a more general setting.