• Journal of the Korea Society for Simulation
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• v.23 no.3
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• pp.11-17
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• 2014

The central composite design (CCD) is often used to estimate the second-order linear model. This paper uses a correlation induction strategy of common random numbers (CRN) in simulation experiment and utilizes the induced correlations to obtain better estimates for the second-order linear model. This strategy assigns the CRN to all design points in the CCD. An appropriate selection of the axial points in CCD makes the weighted least squares (WLS) estimator be equivalent to ordinary least squares (OLS) estimator in estimating the linear model parameters of CCD. We analytically investigate the efficiency of this strategy in estimation of model parameters. Under certain conditions, this correlation induction strategy yields better results than independent random number strategy in estimating model parameters except intercept. The simulation experiment on a selected model supports such results. We expect a suggested random number assignment is useful in application of CCD in simulation experiments.

• Proceedings of the Korea Society for Simulation Conference
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• 1992.10a
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• pp.8-8
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• 1992

Through a simulation experiment, often an experimenter is concerned with estimating the system parameters of the linear model consisting of m design points from the outputs oft the simulation model. To improve the estimation of the system parameters and reliability of these estimators, appropriate simulation techniques have been developed. For the first order linear model, Schruben and Margolin (1978) exploited the random number assignment rules which uses a combination of common random numbers and antithetic streams in a simulation experiment designed to estimate the system parameters when the design matrix of simulation model admits orthogonal blocking into two blocks. Nozari, Arnold and Pegden (1984) developed a method for appliying the method of control variates to the situation of the linear model having multiple design points. This talk deals with a different way of utilizing controls under the correlation induction strategy of Schruben and Margolin's to improve the simulation efficiency, and presents a procedure for obtaining the estimators of the system parameters analytically. Simulation results on a selected simulation model indicate a promising evidence that a proposed method may yield better results than Schruben and Margolin's method.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4367-4372
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• 2014

We here document discovery of expression profile of myeloid derived suppressor cells (MDSCs) in chronic hepatitis B (CHB) patients and changes in the course of disease. The study population was composed of 75 outpatient HBV cases and 15 healthy control cases. Peripheral blood samples were collected for separation of mononuclear cells. Levels of MDSCs labeled with Lin-DR-CD11b+CD33+ obtained from peripheral blood mononuclear cells (PBMC), were revealed to have significant differences between the CHB and other groups. They were 0.414% for health control cases and 0.226% for CHB cases (Z=-2.356, p=0.0189). It also observed that the group of HBeAg positive cases had significant difference in MDSCs/PBMC median ($X^2=11.877$, p=0.003), compared with group of HBeAg negative cases and the healthy control group. It suggested considerable MDSCs might be involved in HBeAg immune tolerance. In addition, negative correlations between MDSCs/PBMC and parameters of ALT, AST and TBil, while positive correlation between MDSCs/PBMC and ALB parameter were found. Multiple comparisons between the four phases and health control phase again, there was a statistically sifnificant difference ($X^2=17.198$, p=0.002). Taken together, these findings may provide a new immunotherapy strategy for reduced the expression levels of MDSCs in CHB patients, through induction of an autoimmune response to virus removal.