• 경영과학
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• 제14권2호
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• pp.69-79
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• 1997

The computational speed of interior point method of linear programming depends on the speed of Cholesky factorization. If the coefficient matrix A has dense columns then the matrix A.THETA. $A^{T}$ becomes a dense matrix. This causes Cholesky factorization to be slow. We study an efficient implementation method of the dense column splitting among dense column resolving technique and analyze the relation between dense column splitting and order methods to improve the sparsity of Cholesky factoror.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.189-194
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• 1989

The sparse vector method is more efficient than conventional sparse matrix method when solving sparse system. This paper considers the structural relation between factorized L and inverse of L and presents a new ordering algorithm for sparse vector method. The method is useful in enhancing the sparsity of the inverse of L while preserving the aparsity of matrix. The performance of algorithm is compared with conventional algorithms by means of several power system.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 춘계 학술발표회 논문집
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• pp.151-156
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• 2003

Variable selection algorithm for principal component analysis using penalized likelihood method is proposed. We will adopt a probabilistic principal component idea to utilize likelihood function for the problem and use HARD penalty function to force coefficients of any irrelevant variables for each component to zero. Consistency and sparsity of coefficient estimates will be provided with results of small simulated and illustrative real examples.

• 대한수학회논문집
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• 제15권4호
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• pp.587-595
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• 2000

We contain a simple construction for the sparse n x n connected orthogonal matrices which have a row of p nonzero entries with 2$\leq$p$\leq$n. Moreover, we study the analogous sparsity problem for an m x n connected row-orthogonal matrices.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.48-54
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• 1995

A direct method for fitting analysis-of-variance models to unbalanced data is presented. This method exploits sparsity and rank deficiency of the matrix and is based on Gram-Schmidt orthogonalization of a set of sparse columns of the model matrix. The computational algorithm of the sum of squares for testing estmable hyphotheses is given.

• Communications for Statistical Applications and Methods
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• 제4권3호
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• pp.881-890
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• 1997

The local linear estimator usually has more attractive properties than Nadaraya-Watson estimator. But the local linear estimator gives bad performance where data are sparse. Muller and Song proposed Shifted Nadaraya Watson estimator which has treated data sparsity well. We show that Shifted Nadaraya Watson estimator has good performance not only in the sparse region but also in the dense region, through the simulation study. Ans we suggest the boundary treatment of Shifted Nadaraya Watson estimator.

• 한국국방경영분석학회지
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• 제29권1호
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• pp.28-42
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• 2003

The simplex method requires basis update in each iteration, which is the most time consuming process. Several methods have been developed for the update of basis which is represented in LU factorized form, such as Bartels-Golub's method, Forrest-Tomlin's method, Reid's method, Saunders's method, etc. In this research, we compare between the updating methods in terms of sparsity, data structure and computing time issues. The analysis is mainly based on the computational experience.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2008년도 춘계학술대회
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• pp.295-299
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• 2008

This paper discusses tests of factor effect or contrast by the use of saturated design $k^n$ factorial design. The nine nonparametric rank measures in normality test using normal probability pot are proposed. Length's PSE(Pseduo Standard Error) test [4] which relies on the concept of effect sparsity is also introduced and extended to the margin of error(ME) and Simultaneous margin of error(SME).

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.77-86
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• 2006

This study is to investigate the error of prediction value with related variables from the recommended system and to examine the error of prediction value with related variables. To decrease the error on the collaborative recommended system on user based, this research explored the effects on the prediction related response pair between raters' demographic variables and Pearson's coefficient and sparsity. The result shows comparative analysis between existing error of prediction value and conditioned one.

• 대한전기학회논문지
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• 제32권10호
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• pp.360-366
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• 1983

In this paper, operation cost of the system is calculated by the probabilistic simulation method. And it is proved that only 20 iterative simulations are enough to get the result obtain by the Monte Carlo simulation method which requires more than 1000 iterative simulations. In the probabilistic simulation method we use the ranking of line contingency which is derived from the line countingency selection algorithm proposed in (2). In using this method the nature of the sparsity of the power system is used.