• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.533-541
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• 2017

Sufficient dimension reduction (SDR) replaces original p-dimensional predictors to a lower-dimensional linearly transformed predictor. The sliced inverse regression (SIR) has the longest and most popular history of SDR methodologies. The critical weakness of SIR is its known sensitive to the numbers of slices. Recently, a fused sliced inverse regression is developed to overcome this deficit, which combines SIR kernel matrices constructed from various choices of the number of slices. In this paper, the fused sliced inverse regression and SIR are compared to show that the former has a practical advantage in survival regression over the latter. Numerical studies confirm this and real data example is presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.281-282
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• 2007

We address a new representation of DCT/DFT/Wavelet matrices via one hybrid architecture. Based on an element inverse matrix factorization algorithm, we show that the OCT, OFT and Wavelet which based on Haar matrix have the similarrecursive computational pattern, all of them can be decomposed to one orthogonal character matrix and a special sparse matrix. The special sparse matrix belongs to Jacket matrix, whose inverse can be from element-wise inverse or block-wise inverse. Based on this trait, we can develop a hybrid architecture.

• Journal of applied mathematics & informatics
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• 제31권3_4호
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• pp.343-352
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• 2013

In this paper, we give a formula of $(P+Q)^D$ under the conditions $P^2Q+QPQ=0$ and $P^3Q=0$. Then applying it to give some results of block matrix $M=(^A_C^B_D)$ (A and D are square matrices) with generalized Schur complement is zero under some conditions. Finally, numerical examples are given to illustrate our results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.252-254
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• 2007

In this paper, we will introduce an encoding algorithm of LDPC Codes in Direct-Sequence UWB systems. We evaluate the performance of the coded systems in an AWGN channel. This new algorithm is based on the Jacket matrics. Mathematically let A = ($a_{kl}$) be a matnx, if $A^{-1}$ = $(a^{-1}_{kl})^r$,then the matrix A is a Jacket matrix. If the Jacket matrices if Low density, the inverse matrices is also Low density which is very important to the introduced encoding algorithm.

• Journal of applied mathematics & informatics
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• 제25권1_2호
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• pp.415-424
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• 2007

The generalized inverses have many important applications in the aspects of theoretic research and numerical computations and therefore they were studied by many authors. In this paper we get some necessary and sufficient conditions of the forward order law for {1}-inverse of multiple matrices products $A\;=\;A_1A_2{\cdots}A_n$ by using the maximal rank of generalized Schur complement.

• 대한수학회지
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• 제47권2호
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• pp.363-372
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• 2010

In this paper, we introduce and study the generalized inverse $A^{(1,2)}_{T,S}$ with the prescribed range T and null space S of an adjointable operator A from one Hilbert $C^*$-module to another, and get some analogous results known for finite matrices over the complex field or associated rings, and the Hilbert space operators.

• Journal of the Korean Statistical Society
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• 제15권1호
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• pp.46-61
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• 1986

Many procedures for deriving the Moore-Penrose invese $X^+$ have been developed, but the explicit forms of Moore-Penerose inverses for design matrices in analysis of variance models are not known heretofore. The purpose of this paper is to find explicit forms of $X^+$ for the one-way and the two-way analysis of variance models. Consequently, the Moore-Penerose inverse $X^+$ and the shortest solutions of them can be easily obtained to the level of pocket calculator by way of our results.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.89-98
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• 1995

This paper presents the interesting properties of a certain patterned matrix that plays an significant role in the multi-way balanced designs. The necessary and sufficient condition on the existence of the inverse of the patterned matrix and its determinant are described. In special cases of the pattermed matrix, explicit formulas for its inverse, determinant and the characteristic equation are obtained.

• Journal of applied mathematics & informatics
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• 제28권3_4호
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• pp.687-696
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• 2010

In this paper, an algorithm for computing the sparse approximate inverse factor of matrix $A^{T}\;A$, where A is an $m\;{\times}\;n$ matrix with $m\;{\geq}\;n$ and rank(A) = n, is proposed. The computation of the inverse factor are done without computing the matrix $A^{T}\;A$. The computed sparse approximate inverse factor is applied as a preconditioner for solving normal equations in conjunction with the CGNR algorithm. Some numerical experiments on test matrices are presented to show the efficiency of the method. A comparison with some available methods is also included.

• Journal of applied mathematics & informatics
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• 제32권1_2호
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• pp.171-184
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• 2014

A higher order iterative method to compute the Moore-Penrose inverses of arbitrary matrices using only the Penrose equation (ii) is developed by extending the iterative method described in [1]. Convergence properties as well as the error estimates of the method are studied. The efficacy of the method is demonstrated by working out four numerical examples, two involving a full rank matrix and an ill-conditioned Hilbert matrix, whereas, the other two involving randomly generated full rank and rank deficient matrices. The performance measures are the number of iterations and CPU time in seconds used by the method. It is observed that the number of iterations always decreases as expected and the CPU time first decreases gradually and then increases with the increase of the order of the method for all examples considered.