• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.719-729
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• 2001

Overlap Maximization is a sampling technique to reduce survey costs and costs associated with the survey. It was first studied by Keyfitz(1951). Ernst(1998) presented a remarkable procedure for maximizing the overlap when the sampling units can be selected for two identical stratified designs simultaneously, But the approach involves mimicking the behaviour of nonlinear function by linear function and so it is less direct, even though the stratification problem for the overlap corresponds directly to the linear programming problem. furthermore, it uses the controlled selection algorithm that repeatedly needs zero-restricted controlled roundings, which are solutions of capacitated transportation problems. In this paper we suggest a comparatively simple procedure to use linear programming in order to maximize the overlap. We show how this procedure can be implemented practically.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.47-62
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• 1992

This paper describes a procedure for optimizing the route selection and production conditions in alternate process plans under a cellular manufacturing environment. The type of production is mainly production-to-order which deals with unexpected products as the changes factor. The flexible cellular manufacturing can be viewed as a complete unification of both flexible manufacturing process and flexible production management. The integrated problem for designing flexible cellular manufacturing associated with determining the optimal values of the machining speeds, overtime, and intercell flow is formulated as Nonlinear Mixed Integer Programming(NMIP) in order to minimize total production change cost. This is achieved by introducing the marginal cost analysis into the NMIP, which will compute the optimal machining speed, overtime, intercell flow, and routing. The application of this procedure offers greater flexibility to take advantage of the cellular manufacturing due to the optimum use of resources. A solution procedure for this problem was developed and a numerical example is included.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.45-53
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• 1995

The Purpose of this paper is to describe and demonstrate a quantitative procedure for evaluating alternative material handling systems that are being considered for possible purchase. The procedure considers both quantitative factors(i.e. costs) and qualitative factors(i.e. equipment productivity) that should be considered in the evaluation process. Since these factors are not always quantifiable it is necessary that the procedure be able to quantify all factors in commensurable units. This is accomplished via a modification of the Brown and Gibson model for facility site location and selection. The model was demonstrated via a case example that encompassed the derivation of performance measures for three material handling systems.

• Journal of Korean Dental Science
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• v.15 no.2
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• pp.112-120
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• 2022

In this article, the contemporary root canal treatment procedure using nickel-titanium (NiTi) instruments was reviewed to understand the correlations between the properties of files and safety of the clinical usage. Literatures were reviewed according to the process of clinical procedure of the root canal preparation, mainly for shaping during orifice flaring, glide-path preparation, and main canal instrumentation. Considering the reasons for NiTi file fracture, clinically implacable issues and ideas were discussed to reduce the fracture risk and increase clinical efficiency of the NiTi file systems. Various kinds of NiTi file systems have their own characteristics and properties given from their geometries and heat treatments and so on. Proper selection and careful usage of the NiTi file systems may reduce the risk of file fracture and increase the efficiency of NiTi file systems. Understanding of the clinical implications from the mechanical properties and characteristics of the engine driven NiTi instruments may decrease the risk of NiTi file fractures and increase the success rate in root canal treatment.

• The Journal of the Korean life insurance medical association
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• v.26
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• pp.31-39
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• 2007

Background and main issue: In the Korean insurance market, an outstanding issue is the decrease of margin of risk ratio. This affects the solvency and profitability of insurance companies. Insurance medicine, which has been developed in Western countries, is so-called medical risk selection or medical underwriting. Medical risk selection is based on clinical follow-up study and mortality analysis methodology. Unfortunately, there have been few clinical follow-up studies, and no intercompany disease analysis system is available in the Korean insurance market. In practice, we use underwriting guidelines, which were developed by some global reinsurance companies. However, these guidelines were developed under clinical follow-up studies performed abroad. So, we cannot rule out underestimation of excess mortality factors such as mortality ratio, excess death rate, and life expectancy. It is necessary to perform medical assessment in claims administration. Comparing the insured's statement by medical records with products' benefit according to this procedure, we can make sound claim decisions and participate in the role of sound underwriting. We can call this scientific procedure as the verification of medical claims review. Another area of medical claims review is medical counsel for claims staff. Result: There is another insurance medicine in addition to medical risk selection. Independent medical assessment by medical records of insured is medical claims review. Medical claims review is composed of verification and counsel.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.113-131
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• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.179-189
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• 2023

Hybrid sufficient dimension reduction (SDR) methods to a weighted mean of kernel matrices of two different SDR methods by Ye and Weiss (2003) require heavy computation and time consumption due to bootstrapping. To avoid this, Park et al. (2022) recently develop the so-called cross-distance selection (CDS) algorithm. In this paper, two variations of the original CDS algorithm are proposed depending on how well and equally the covk-SAVE is treated in the selection procedure. In one variation, which is called the larger CDS algorithm, the covk-SAVE is equally and fairly utilized with the other two candiates of SIR-SAVE and covk-DR. But, for the final selection, a random selection should be necessary. On the other hand, SIR-SAVE and covk-DR are utilized with completely ruling covk-SAVE out, which is called the smaller CDS algorithm. Numerical studies confirm that the original CDS algorithm is better than or compete quite well to the two proposed variations. A real data example is presented to compare and interpret the decisions by the three CDS algorithms in practice.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1329-1341
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• 2006

When X and Y have independent gamma distributions, we consider the testing problem for two gamma means. We propose a solution based on a Bayesian model selection procedure to this problem in which no subjective input is considered. The reference prior is derived. Using the derived reference prior, we compute the fractional Bayes factor and the intrinsic Bayes factors. The posterior probability of each model is used as a model selection tool. Simulation study and a real data example are provided.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1371-1374
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• 1987

This paper presents a new automatic multithreshold selection method which is based on the threshold selection method proposed by Otsu. This method can overcome some of limitations of the Otsu's method. An optimal threshold is selected by the new criterion so as to maximize the separability in all subregions. To get multiple thresholds, the procedure may be recursively applied to the resultant classes which are determined by the proposed evaluation measure.