• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.125-134
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• 2006

This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.195-203
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• 1996

A bootstrap method is used to correct the apparent downward bias of a naive plug-in bootstrap model selection criterion, which is shown to enjoy a high degree of accuracy. Comparison of bootstrap method with the asymptotic method is made through an illustrative example.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.265-267
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• 2012

This paper analyzes network selection issues of secondary users (SUs) in Cooperative Cognitive Radio Networks (CRNs) by utilizing Queuing Model. Coordinating with Handover Cost-Based Network selection, this paper also addresses an opportunity for the secondary users (SUs) to enhance QoS as well as economics efficiency. In this paper, network selection of SUs is the optimal association between Overall System Time Minimization Problem evaluation of Secondary Connection (SC) and Handover Cost-Based Network selection. This will be illustrated by simulation results.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• Korean Management Science Review
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• v.17 no.2
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• pp.115-124
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• 2000

Activity-Based Costing(ABC) is an accounting cost system which allocates the overhead cost to each cost object more accurately. ABC system achieves improved accuracy in estimating the cost of cost object by using multiple cost drivers to trace the cost of activities to the cost objects associated with the resources consumed by those activities. The selection and the aggregation of these cost driver candidates can pose difficult problems. This paper deals with these problems in mathematical programming approach. The first model is formulated as an integer programming model in cost driver selection and the second model is formulated as multi-objective goal programming model in reduction of cost drivers already selected.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.118-128
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• 2017

Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.79-90
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• 1995

The material handling equipment selection, that is a key task in the material handling system design, is a complex, difficult task, and requires a massive technical knowledge and systematic analysis. It is invaluable to justify the selected equipment model by the performance evaluation before its actual implementation. This paper presents an intelligent knowledge-based expert system called "IMESE" created by authors, for the selection and evaluation of material handling equipment model suitable for movement and storage of materials in a manufacturing facility. The IMESE is consisted of four modules: a knowledge base to select an appropriate equipment type, a multiple criteria decision making procedure to choose the most favorable commercial model of the selected equipment type, a database to store the list of commercial models of equipment types with their specifications, and simulators to evaluate the performance of the equipment model. The whole process of IMESE is executed under VP-Expert expert system environment.vironment.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.301-311
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• 2020

Schwarz's Bayesian information criterion (BIC) is one of the most popular criteria for model selection, that was derived under the assumption of independent and identical distribution. For correlated data in longitudinal studies, Jones (Statistics in Medicine, 30, 3050-3056, 2011) modified the BIC to select the best linear mixed effects model based on the effective sample size where the number of parameters in covariance structure was not considered. In this paper, we propose an extended Jones' modified BIC by considering covariance parameters. We conducted simulation studies under a variety of parameter configurations for linear mixed effects models. Our simulation study indicates that our proposed BIC performs better in model selection than Schwarz's BIC and Jones' modified BIC do in most scenarios. We also illustrate an example of smoking data using a longitudinal cohort of cancer patients.

• Journal of the Korea Institute of Building Construction
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• v.8 no.4
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• pp.61-69
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• 2008

The size of the construction projects become huge and complex, and the depth of excavation for the underground structures become deeper. Also the working area is not enough for loading materials and temporary facilities. This is the most case of recent construction projects in downtown area. Top-down is the most useful method for this kind of construction projects. Top-down construction method consists of supporting method, retaining wall type, foundation type and construction direction such as up-down or up-up. construction managers have to select sub-elements for top-down construction method in planning phase. This study is to suggest the sub-elements selection model for the top-down construction method, and the case study is conducted for evaluating this model.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.167-182
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• 1999

This article is concerned with the selection of subsets of predictor variables to be included in building the binary response probit regression model. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the probit regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. The appropriate posterior probability of each subset of predictor variables is obtained through the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as the one with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.