Characterizations of Noncentral Chi-Squared-Generating Covariance Structures for a Normally Distributed Random Vector

Phil D. Young () and Dean M. Young
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Phil D. Young: Baylor University
Dean M. Young: Baylor University

Sankhya A: The Indian Journal of Statistics, 2016, vol. 78, issue 2, No 5, 247 pages

Abstract: Abstract Let y ∼ N n μ , V ${\mathbf {y}} \sim N_{n}\left ({\boldsymbol {\mu }}, {\mathbf {V}} \right )$ , 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 y ′ A i y i = 1 k $\left \{{\mathbf {y}}^{\prime } {\mathbf {A}}_{i}{\mathbf {y}} \right \}^{k}_{i=1}$ for i ∈ 1 , ... , k $i \in \left \{1,...,k \right \}$ , 2≤k≤n, is distributed as multiples of mutually independent non-central chi-squared random variables. We consider the case when the A i ’s and V are both nonnegative definite, including several cases where the A i ’s have special properties, and the case where the A i ’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).

Keywords: Generalized inverse; Moore-Penrose pseudo-inverse; Non-central chi-squared random variable; Nonnegative-definite covariance matrix; Positive-definite covariance matrix; 15A24; 15A09; 62H10 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s13171-016-0081-3

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