• Communications for Statistical Applications and Methods
• /
• v.19 no.5
• /
• pp.685-696
• /
• 2012

These days the interest of quality leads to the necessity of control charts for monitoring the process in various fields of practical applications. The $\overline{X}$ chart is one of the most widely used tools for quality control that also performs well under the normality of quality characteristics. However, quality characteristics tend to have nonnormal properties in real applications. Numerous recent studies have tried to find and explore the performance of $\overline{X}$ chart due to non-normality; however previous studies numerically examined the effects of non-normality and did not provide any theoretical justification. Moreover, numerical studies are restricted to specific type of distributions such as Burr or gamma distribution that are known to be flexible but can hardly replace other general distributions. In this paper, we approximate the false alarm rate(FAR) of the $\overline{X}$ chart using the Edgeworth expansion up to 1/n-order with the fourth cumulant. This allows us to examine the theoretical effects of nonnormality, as measured by the skewness and kurtosis, on $\overline{X}$ chart. In addition, we investigate the effect of skewness and kurtosis on $\overline{X}$ chart in numerical studies. We use a skewed-normal distribution with a skew parameter to comprehensively investigate the effect of skewness.

• Communications for Statistical Applications and Methods
• /
• v.27 no.5
• /
• pp.535-546
• /
• 2020

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.55-64
• /
• 2021

In the last few years, the massive development in wireless networks, high internet speeds and improvement in car manufacturing has shifted research focus to Vehicular Ad-HOC Networks (VANETs). Consequently, many related frameworks are explored, and it is found that security is the primary issue for VANETs. Despite that, a small number of research studies have taken into consideration the identification of performance standards and parameters. In this paper, VANET security frameworks are explored, studied and analysed which resulted in the identification of a list of performance evaluation parameters. These parameters are defined and categorized based on the nature of parameter (security or general context). These parameters are identified to be used by future researchers to evaluate their proposed VANET security frameworks. The implementation paradigms of security frameworks are also identified, which revealed that almost all research studies used simulation for implementation and testing. The simulators used in the simulation processes are also analysed. The results of this study showed that most of the surveyed studies used NS-2 simulator with a percentage of 54.4%. The type of scenario (urban, highway, rural) is also evaluated and it is found that 50% studies used highway urban scenario in simulation.

• Bulletin of the Korean Mathematical Society
• /
• v.42 no.3
• /
• pp.509-520
• /
• 2005

The author studies the asymptotic behavior of the radial minimizer for a variant of the p-Ginzburg-Landau type functional, in the case of p larger than the dimension, when the parameter tends to zero. The C$^{1, convergence of the radial minimizer is proved. And the estimation of the convergent rate of the minimizer is given.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.2
• /
• pp.153-165
• /
• 2003

This paper considers the likelihood ratio test statistic based on nonlinear regression quantiles estimators in order to test of hypothesis about the regression parameter $\theta_o$ and derives asymptotic distribution of proposed test statistic under the null hypothesis and a sequence of local alternative hypothesis. The paper also investigates asymptotic relative efficiency of the proposed test to the test based on the least squares estimators or the least absolute deviation estimators and gives some examples to illustrate the application of the main result.

• The Korean Journal of Applied Statistics
• /
• v.8 no.2
• /
• pp.187-200
• /
• 1995

This paper is concerned with a regression model with nonlinear time series errors. Testing procedures for linearity of error terms are studied. To this end, large-sample properties of estimators of regression parameters and autoregression parameter are obtained. These results are then used to develop test statistics for testing linearity of errors. Some simulation studies are shown.

• Proceedings of the KIEE Conference
• /
• 1991.07a
• /
• pp.379-382
• /
• 1991

The dynamic characteristics and mathematical models of the KEPCO's generator control systems have been derived by on-line tests. Measuring the responses for small disturbances in the inputs, the parameters of governing systems, excitation systems, and power system stabilizers have been determined. An overview of the field tests, parameter identification, and model verification is presented.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.730-739
• /
• 2005

A common problem encountered in product or process design is the selection of optimal parameter levels which involve simultaneous consideration of multiresponse variables. A multiresponse problem is solved through three major stages: data collection, model building, and optimization. To date, various methods have been proposed for the optimization stage, including the desirability function approach and loss function approach. In this paper, we first propose a framework classifying the existing studies and then propose some promising directions for future research.

• Communications for Statistical Applications and Methods
• /
• v.21 no.4
• /
• pp.297-307
• /
• 2014

We derive a test statistic for the general linear test in the ridge regression model. The exact distribution for the test statistic is too difficult to derive; therefore, we suggest an approximate reference distribution. We use numerical studies to verify that the suggested distribution for the test statistic is appropriate. A asymptotic result for the test statistic also is considered.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.754-759
• /
• 1993

An adaptive model predictive control (AMPC) strategy using auto-regression moving-average (ARMA) models is presented. The characteristic features of this methodology are the small computer memory requirement, high computational speed, robustness, and easy handling of nonlinear and time varying MIMO systems. Since the process dynamic behaviors are expressed by ARMA models, the model parameter adaptation is simple and fast to converge. The recursive least square (RLS) method with exponential forgetting is used to trace the process model parameters assuming the process is slowly time varying. The control performance of the AMPC is verified by both comparative simulation and experimental studies on distillation column control.