• Journal of the Korean Statistical Society
• /
• v.17 no.1
• /
• pp.1-8
• /
• 1988

Kernel estimates of an unknown regression function are studied. Bandwidth selection rule minimizing integrated absolute error loss function is considered. Under some reasonable assumptions, it is shown that the optimal bandwidth is unique and can be computed by using bisection algorithm. Adaptive bandwidth selection rule is proposed.

• Journal of the Korean Data and Information Science Society
• /
• v.27 no.6
• /
• pp.1653-1660
• /
• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• KNOM Review
• /
• v.21 no.2
• /
• pp.35-45
• /
• 2018

As Network Slicing functionality gets applied to mobile networking, a mechanism that enables the selection of network slices becomes indispensable. Following the 3GPP Technical Specification for the 5G Architecture, the inclusion of the Network Slice Selection Function (NSSF) in order to leverage the process of slice selection is apparent. However, actual implementation of this network function needs to deal with the dynamic changes of network instances, due to this, a platform that supports the orchestration of Virtual Network Functions (VNF) is required. Our proposed solution include the use of the Central Office Rearchitected as a Data Center (CORD) platform, with the specified profile for mobile networks (M-CORD) that integrates a service orchestrator (XOS) alongside solutions oriented to Software Defined Networking (SDN), Network Function Virtualization (VNF) and virtual machine management through OpenStack, in order to provide the right ecosystem where our implementation of NSSF can obtain slice information dynamically by relying on synchronization between back-end services and network function instances.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.3
• /
• pp.613-618
• /
• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
• /
• v.24 no.6
• /
• pp.673-683
• /
• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.710-712
• /
• 2004

Generation companies(Genco) submit the supply functions as a bidding function to a bid market in a competitive electricity market. The profits of Gencos vary in accordance with the bid functions, so the selection of a bidding function plays a key role in increasing their profits. This paper presents an analysis of the selection of the supply function from the viewpoint of Nash equilibrium(NE). Four types of bidding function parameters are used for analizing the electricity market. The competition of selecting bidding parameters is modeled as subgame and overall game in this research. The NEs in both game are computed by using analytic method and payoff matrix method. It is verified in case studies for the NE of overall game to satisfy the equilibrium condition.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.32A no.6
• /
• pp.104-110
• /
• 1995

In this paper, an effective method for the minimization of two-level Reed-muller expressions by cube selection whcih considers functional complexity is presented. In contrast to the previous methods which use Xlinking operations to join two cubes for minimizatio, the cube selection method tries to select cubes one at a time until they cover the ON-set of the given function. This method works for most benchmark circuits, but for parity-type functions it shows power performance. To solve this problem, a cost function which computes the functional complexity instead of only the size of ON-set of the function is used. Therefore the optimization is performed considering how the trun minterms are grouped together so that they can be realized by only a small number of cubes. In other words, it considers how the function is changed and how the change affects the next optimization step. Experimental results shows better performance in many cases including parity-type functions compared to pervious results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.547-548
• /
• 2021

In this paper, we consider inner inheritance of parent objects to their child objects in SR DEVS. The inheritance process of function is implemented by the inside inheritance function when the inherited parent object is determined. In the inside inheritance, the integrity and fragment inheritance can be divided as th asset property. Also, the function process is requested when the inheritance selection in th inheritance is occurred to several assets. The child object can receive assets from the parent object through the determination method of selection function to the inside inheritance.

• Journal of Korea Water Resources Association
• /
• v.39 no.6 s.167
• /
• pp.495-502
• /
• 2006

The importance of the bandwidth selection has been more emphasized than the kernel function selection for nonparametric frequency analysis since the interpolation is more reliable than the extrapolation method. However, when the extrapolation method is being applied(i.e. recurrence interval more than the length of data or extreme probabilities such as $200{\sim}500$ years), the selection of the kernel function is as important as the selection of the bandwidth. So far, the existing kernel functions have difficulties for extreme value estimations because the values extrapolated by kernel functions are either too small or too big. This paper suggests a Modified Cauchy kernel function that is suitable for both interpolation and extrapolation as an improvement.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.951-959
• /
• 2011

The proposed method is based on a penalized censored regression model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log likelihood function of censored regression model. It provide the efficient computation of regression parameters including variable selection and leads to the generalized cross validation function for the model selection. Numerical results are then presented to indicate the performance of the proposed method.