• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.135-145
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• 1997

The statistical approach to quality control aims at alerting its user to any variations in the properties of a manufactured product. Motorola developed and pursued a quality management program called six sigma. The goal of six sigma programs is to improve customer satisfaction through reducing and eliminating defects. six sigma uses several statistical measure to characterize defect levels and process capabilities. The upper and lower specification limits are $\pm6\sigma$(sigma) from nominal, and the process mean is centered at nominal. only 0.002PPM are outside specification limits. Cp=2. this is the design target in a six sigma program. This article presents an important tool available for quality control of a production process at the occurrence of defects in manufactured products at view low levels to improve the efficiency of the manufacturing productivity and to satisfy customer through the reduction of defect rates. To understand the consequences of the level of quality on competitive position, a more technical perspective is needed.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.339-351
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• 1998

Designs for discriminating between two linear regression models are studied under $\Lambda$-type optimalities maximizing the measure for the lack of fit for the designs with fixed model inadequacy. The problem of selecting an appropriate $\Lambda$-type optimalities is shown to be closely related to the estimation method. $\Lambda$-type optimalities for the least squares and minimum bias estimation methods are considered. The minimum bias designs are suggested for the designs invariant with respect to the two estimation methods. First order minimum bias designs optimal under $\Lambda$-type optimalities are then derived. Finally for the case where the lack of fit test is significant, an approach to the construction of a second order design accommodating the optimal first order minimum bias design is illustrated.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.277-289
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• 2003

We propose modified exact inferential methods in logistic regression model. Exact conditional distribution in logistic regression model is often highly discrete, and ordinary exact inference in logistic regression is conservative, because of the discreteness of the distribution. For the exact inference in logistic regression model we utilize the modified P-value. The modified P-value can not exceed the ordinary P-value, so the test of size $\alpha$ based on the modified P-value is less conservative. The modified exact confidence interval maintains at least a fixed confidence level but tends to be much narrower. The approach inverts results of a test with a modified P-value utilizing the test statistic and table probabilities in logistic regression model.

• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.229-235
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• 2007

We consider a ruin model whose surplus process is formed by a compound Poisson process. If the level of surplus reaches V > 0, it is assumed that a certain amount of surplus is invested. In this paper, we apply the optional sampling theorem to the surplus process and obtain the expectation of period T, time from origin to the point where the level of surplus reaches either 0 or V. We also derive the total and average amount of surplus during T by establishing a backward differential equation.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.294-299
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• 2005

We have studied unbound docking for 12 protein-protein complexes using conformational space annealing (CSA) combined along with statistical pair potentials. The CSA, a powerful global optimization tool, is used to search the conformational space represented by a translational vector and three Euler amgles between two proteins. The energy function consists of three statistical pair-wise energy terms; one from the distance-scaled finite ideal-gas reference state (DFIRE) approach by Zhou and the other two derived from residue-residue contacts. The residue-residue contact terms describe both attractive and repulsive interactions between two residues in contact. The performance of the CSA docking is compared with that of ZDOCK, a well-established protein-protein docking method. The results show that the application of CSA to the protein-protein docking is quite successful, indicating that the CSA combined with a good scoring function is a promising method for the study of protein-protein interaction.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.136-144
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• 2006

Aluminum engine piston is manufactured by thixoforging according to forming variables. It is very important to find effects of forming variables on final products in thixoferging. In order to find the effects, however, many researchers and industrial technicians have depended upon too many types of experiments. In this study, the process parameters which have influences on thixofurging process of aluminum automotive engine piston are found by a statistical method and the correlation equations between the process parameters and quality of product are approximated through the surface response analysis. Forming variables such as initial solid fraction, die temperature, and compression holding time are considered fur manufacturing aluminum engine piston by thixofurging. Hardness and microstructure are inspected so that optimal forming condition is found by the statistical approach.

• Journal of the Korean Statistical Society
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• v.21 no.2
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• pp.93-110
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• 1992

A model selection approach is used to find out whether the mean and the variance of a unique sample are different from the pre-specified values. Normal distribution is selected as an approximating model. Kullback-Leibler discrepancy comes out as a natural measure of discrepancy between the operating model and the approximating model. Several estimates of selection criterion are computed including AIC, TIC, and a coupleof bootstrap estimator of the selection criterion are considered according to the way of resampling. It is shown that a closed form expression is available for the parametric bootstrap estimated cirterion. A Monte Carlo study is provided to give a formal comparison when the operating family itself is normally distributed.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.783-795
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• 2005

We consider to obtain an estimate of bivariate survival function for the right censored data with the assumption that the two components of censoring vector are independent. The estimate is derived from an ad hoc approach based on the representation of survival function. Then the resulting estimate can be considered as an extension of the Susarla- Van Ryzin estimate to the bivariate data. Also we show the consistency and weak convergence for the proposed estimate. Finally we compare our estimate with Dabrowska's estimate with an example and discuss some properties of our estimate with brief comment on the extension to the multivariate case.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.193-200
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• 2000

Modelling the dependence via random effects in censored multivariate survival data has recently received considerable attention in the biomedical literature. The random effects models model not only the conditional survival times but also the conditional hazard rate. Systematic likelihood inference for the models with random effects is possible using Lee and Nelder's (1996) hierarchical-likelihood (h-likelihood). The purpose of this presentation is to introduce Ha et al.'s (2000a,b) inferential methods for the random effects models via the h-likelihood, which provide a conceptually simple, numerically efficient and reliable inferential procedures.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.115-142
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• 2006

This paper studies the problem of option hedging when the underlying asset price process is a compound Poisson process. By adopting an asymptotic approach to let the security price converge to a continuous process, we find a closed-form hedging strategy that improves the classical Black-Scholes hedging strategy in a quadratic sense. We first show that the scaled Black-scholes hedging error has a limit in law, and that limit is decomposed into a part that can be traded away and a part that is purely unreplicable. The Black-Scholes hedging strategy is then modified by adding the replicable part of its hedging error and by adding the mean-variance hedging strategy to the nonreplicable part. Some results of simulation experiment s are also provided.