• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.165-175
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• 2002

We consider the problem of estimating the change-point in mean change model with the one change-point. Lombard (1987) suggested change-point estimation based on score functions. Gombay and Huskova (1998) derived a class of change-point estimators with the score function of rank. Various change-point estimators with the log score functions of ranks are suggested and compared via simulation.

• Journal of the Korean Mathematical Society
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• v.53 no.5
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• pp.969-990
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• 2016

We derive a sampling theorem associated with first order self-adjoint eigenvalue problem with a finite rank perturbation. The class of the sampled integral transforms is of finite Fourier type where the kernel has an additional perturbation.

• Korean Journal of Metals and Materials
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• v.46 no.9
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• pp.554-562
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• 2008

The distribution and prediction interval for the misorientation angle of grain boundary at which $Cr_2N$ was precipitated during heating at $900^{\circ}C$ for $10^4$ sec were newly estimated, and followed by the estimation of mathematical and median rank methods. The probability density function of the misorientation angle can be estimated by a statistical analysis. And then the ($1-{\alpha}$)100% prediction interval of misorientation angle obtained by the estimated probability density function. If the estimated probability density function was symmetric then a prediction interval for the misorientation angle could be derived by the estimated probability density function. In the case of non-symmetric probability density function, the prediction interval could be obtained from the cumulative distribution function of the estimated probability density function. In this paper, 95, 99 and 99.73% prediction interval obtained by probability density function method and cumulative distribution function method and compared with the former results by median rank regression or mathematical method.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.432-436
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• 2009

We have developed a new optimization algorithm termed simple bacteria cooperative optimization (sBCO) based on bacteria behavior patterns [1]. In [1], we have introduced the algorithm with basic operations and showed its feasibility with some function optimization problems. Since the sBCO was the first version with only basic operations, its performance was not so good. In this paper, we adopt a new operation, rank replacement, to the sBCO for improving its performance and compare its results to those of the simple genetic algorithm (sGA) which has been well known and widely used as an optimization algorithm. It was found from the experiments with four function optimization problems that the sBCO with rank replacement was superior to the sGA. This shows that our algorithm can be a good optimization algorithm.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.574-574
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.575-583
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• 2000

Consider the problem of estimating regression function from a set of data which is contaminated by a long-tailed error distribution. The linear smoother is a kind of a local weighted average of response, so it is not robust against outliers. The kernel M-smoother and the lowess attain robustness against outliers by down-weighting outliers. However, the kernel M-smoother and the lowess requires the iteration for computing the robustness weights, and as Wang and Scott(1994) pointed out, the requirement of iteration is not a desirable property. In this article, we propose the robust nonparametic regression method which does not require the iteration. Robustness can be achieved not only by down-weighting outliers but also by transforming outliers. The rank transformation is a simple procedure where the data are replaced by their corresponding ranks. Iman and Conover(1979) showed the fact that the rank transformation is a robust and powerful procedure in the linear regression. In this paper, we show that we can also use the rank transformation to nonparametric regression to achieve the robustness.

• Biomedical Science Letters
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• v.23 no.4
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• pp.295-302
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• 2017

Mononuclear osteoclast precursors derived from hematopoietic progenitors fuse together and then become multinucleated mature osteoclasts by macrophage-colony stimulating factor (M-CSF) and receptor activator of nuclear factor-${\kappa}B$ ligand (RANKL). Especially, the binding of RANKL to its receptor RANK provides key signals for osteoclast differentiation and bone-resorbing function. RANK transduces intracellular signals by recruiting adaptor molecules such as TNFR-associated factors (TRAFs), which then activate mitogen activated protein kinases (MAPKs), Src/PI3K/Akt pathway, nuclear factor-${\kappa}B$ (NF-${\kappa}B$) and finally amplify NFATc1 activation for the transcription and activation of osteoclast marker genes. This review will briefly describe RANKL-RANK signaling pathways and key molecules critical for osteoclast differentiation.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.67-71
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• 2009

This paper presents a rank-constrained linear matrix inequality (LMI) approach to the design of a multi-objective controller such as $H_2/H_{\infty}$ control. Multi-objective control is formulated as an LMI optimization problem with a nonconvex rank condition, which is imposed on the controller gain matirx not Lyapunov matrices. With this rank-constrained formulation, we can expect to reduce conservatism because we can use separate Lyapunov matrices for different control objectives. An iterative penalty method is applied to solve this rank-constrained LMI optimization problem. Numerical experiments are performed to illustrate the proposed method.

• Journal of architectural history
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• v.16 no.2
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• pp.79-98
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• 2007

The building site was presumed as the Howonjeokwon or the Dasiwonji established at the traffic route among western inland areas in old Pyeonghaegun. The groundwork was composed inclination terrace of two steps. The upper step was the territory of the main house of ceremony symbolic+lodging function and the down step was divided in the territory of both Ikrang-chae of boarding and lodging management function and the territory of Haengrang-chae pavilion of rest reception management. The building composition by each territory formed space differentiation with the line of flow according to the thorough rule of court rank. The arrangement composition set the strong center axis in tandem, and it was the building arrangement interpreting the territory and the rule of court rank by function as the relationship of master and servant and was the building type emphasizing on the whole unity with the geometric symmetry structure. The Dasiwonji and the Hyeeumwonji had the common point which is the traffic architecture of semi governmental management reinforcing the boarding lodging function in the place which a visitor stayed. The Haeeumwonji and Dasiwonji had the difference in the size, but was the same in the composition system of construction. The external space and the system of the line of flow decided from the functional territory separation and the hierarchical rank as thorough as the geometric arrangement focusing on the whole unity. Namely, it was the strict and unusual system of the line of flow depended on the thorough rule of court rank than the efficient approach or arrangement of the line of flow. After being established to be the Howonjeokwon at the end of Koryeo, it disappeared at the time of the invasion of Japanese pirates when it was the time of the King Woo in Koryeo. It is presumed that it was continued until both wars, Imjin Byeongja, ago after changing its name to the Dasiwon, being restored to the small size at the end of Koryeo at the early of Choseon.