• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.24 no.1
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• pp.85-91
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• 2020

In this parer we express the sufficient conditions under which it is proved that a J-normal irreduciable Hessenberg matrix is tridiagonal and it is also proved that a similar statement exists for J-conjugate normal matrices

• Journal of the Korean Mathematical Society
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• v.43 no.1
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• pp.215-224
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• 2006

We call the bipartite graph G is normal provided the reduced adjacency matrix A of G is normal. In this paper we give graph representations of normal matrices. In addition we shall have the characterization of signed bipartite normal graphs.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.23 no.3
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• pp.267-282
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• 2019

This paper examines how one can build a block tridiagonal structure for k-almost normal matrices and also for k-almost conjugate normal matrices. We shall see that these representations are created by unitary similarity and unitary congruance transformations, respectively. It shall be proven that the orders of diagonal blocks are 1, k + 2, 2k + 3, ${\ldots}$, in both cases. Then these block tridiagonal structures shall be reviewed for the cases where the mentioned matrices satisfy in a second-degree polynomial. Finally, for these processes, algorithms are presented.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.475-484
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• 2004

In general, a matrix A is idempotent if $A^2$ = A. The idempotent matrices play an important role in the matrix theory and some properties of the Boolean matrices are examined. Using the upper diagonal completion process, we give the characterization of the Boolean idempotent matrices in modified Frobenius normal form.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.929-938
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• 1999

We propose a criterion for testing homogeneity of diagonal covariance matrices of K multivariate normal populations. It is based on a factorization of usual likelihood ratio intended to propose and develop a criterion that makes use of properties of structures of the diagonal convariance matrices. The criterion then leads to a simple test as well as to an accurate asymptotic distribution of the test statistic via general result by Box (1949).

• Korean Journal of Mathematics
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• v.7 no.1
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• pp.53-60
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• 1999

We find bounds of eigenvalues of bipartite tournament matrices. We see when bipartite matrices exist and how players and teams of the matrices are evenly ranked. Also, we show that a bipartite tournament matrix can be both regular and normal when and only when it has the same team size.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.641-651
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• 2011

In this paper, with use of the displacement matrix, two special matrices are constructed. By these special matrices the block decompositions of the block symmetric Hankel matrix and the inverse of the Hankel matrix are derived. Hence, the algorithms according to these decompositions are given. Furthermore, the numerical tests show that the algorithms are feasible.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.55-64
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• 2000

We propose a criterion for testing homogeneity of matrix intraclass covariance matrices of K multivariate normal populations, It is based on a variable transformation intended to propose and develop a likelihood ratio criterion that makes use of properties of eigen structures of the matrix intraclass covariance matrices. The criterion then leads to a simple test that uses an asymptotic distribution obtained from Box's (1949) theorem for the general asymptotic expansion of random variables.

• Bulletin of the Korean Mathematical Society
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• v.43 no.1
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• pp.1-7
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• 2006

In this paper, we investigate bounded matrices over regular rings. We observe that every bounded matrix over a regular ring can be described by idempotent matrices and invertible matrices. Let A, $B{in}M_n(R)$ be bounded matrices over a regular ring R. We prove that $(AB)^d = U(BA)^dU^{-1}$ for some $U{\in}GL_n(R)$.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.