# Gyo-Young Cho's research while affiliated with Kyungpook National University and other places

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## Publications (20)

A start-up demonstration test is a mechanism that is usually used to determine the reliability of equipment, for example water pumps, car batteries and power generators. The simplest and oldest start-up demonstration tests are called CS (consecutive successes) which have been studied by Hahn and Gage (1983), Viveros and Balakrishnan (1993).At first...

This paper is a study on the multivariate CUSUM control charts using three different control statistics for monitoring covariance matrix. We get control limits and ARLs of the proposed multivariate CUSUM control charts using three different control statistics by using computer simulations. The performances of these proposed multivariate CUSUM contr...

Outliers in surveys can have a large effect on estimates of totals. This is especially true in business surveys where the populations are drawn are typically skewed. In this paper, we discussed the practical development and implementation of methods to identify and deal with outliers. A detection method is based on quartile method and detected outl...

A control chart is very useful in monitoring various production process. There are many situations in which the simultaneous control of two or more related quality variables is necessary. We construct bivariate Shewhart control charts based on the trace of the product of the estimated variance-covariance matrix and the inverse of the in-control mat...

Modern production process is a very complex structure combined observations which are correlated with several factors. When the error signal occurs in the process, it is very difficult to know the root causes of an out-of-control signal because of insufficient information. However, if we know the time of the change, the system can be controlled mor...

Qu et. al. (2000) proposed the quadratic inference functions (QIF) method to marginal model analysis of longitudinal data to improve the generalized estimating equations (GEE). It yields a substantial improvement in efficiency for the estimators of regression parameters when the working correlation is misspecified. But for the longitudinal data wit...

The quadratic inference functions (QIF) method proposed by Qu et al. (2000) and the generalized method of moments (GMM) for marginal regression analysis of longitudinal data with time-dependent covariates proposed by Lai and Small (2007) both are the methods based on generalized method of moment (GMM) introduced by Hansen (1982) and both use genera...

For the marginal model and generalized estimating equations (GEE) method there is important full covariates conditional mean (FCCM) assumption which is pointed out by Pepe and Anderson (1994). With longitudinal data with time-varying stochastic covariates, this assumption may not necessarily hold. If this assumption is violated, the biased estimate...

We know that the exponentially weighted moving average (EWMA) control charts are sensitive to detecting relatively small shifts. Multivariate EWMA control charts are considered for monitoring of variance-covariance matrix when the distribution of process variables is multivariate normal. The performances of the proposed EWMA control charts are eval...

Multivariate Shewhart control charts are considered for the simultaneous monitoring the variance-covariance matrix when the joint distribution of process variables is multivariate normal. The performances of the multivariate Shewhart control charts based on control statistic proposed by Hotelling (1947) are evaluated in term of average run length (...

Traditional control charts for process monitoring are fixed sampling rate (FSR) control charts based on taking samples of fixed size with a fixed sampling interval between samples. Variable sampling rate (VSR) control charts vary the sampling rate as a function of the data from the process, and can detect most process changes significantly faster t...

This paper considers the multivariate integrated process control procedure for detecting special causes in a multivariate IMA(1, 1) process. When the multivariate control chart signals, the special cause will be detected and eliminated from the process. However, when the elimination of the special cause costs high or is not practically possible, an...

In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart is often used as an alternative charting...

The need for statistical process control to check the performance of a process is becoming more important in chemical and pharmaceutical industries. This study illustrates the method to determine whether a process is in control and how to produce and interpret control charts. In the experiment, a stream of green dyed water and a stream of pure wate...

The objective of this paper is to develop variable sampling interval multivariate control charts that can offer significant performance improvements compared to standard fixed sampling rate multivariate control charts. Most research on multivariate control charts has concentrated on the problem of monitoring the process mean, but here we consider t...

In statistical process control, the primary method used to monitor the number of nonconformities is the c-chart. The conventional c-chart is based on the assumption that the occurrence of nonconformities in samples is well modeled by a Poisson distribution. When the Poisson assumption is not met, the X-chart is often used as an alternative charting...

This study was conducted to investigate whether mat exercise and field exercise have effectiveness on the balance and gait in older adults. Thirty subjects were participated in this study. And they were all randomly divided into mat exercise and field exercise group. To evaluate the effects of mat and field exercise, subjects were evaluated by usin...

This paper is the generalization of the modified two-level skip-lot sampling plan(MTSkSP2) to n-level. The general formulas of the operating characteristic(OC) function, average sample number(ASN) and average outgoing quality(AOQ) for the plan are derived using Markov chain properties.

Multivariate control charts are considered for the simultaneous monitoring of the mean vector and covariance matrix when the joint distribution of process variables is multivariate normal. Emphasis is on the use of combinations of multivariate exponentially weighted moving average (MEWMA) control charts based on sample means and on the sum of the s...

In this paper a new process incapability index is introduced for non-normal process. is proposed by transformation of the . The use of each technique is illustrated by reference to a distribution system which includes the Pearson and Johnson functions. Accordingly, is proposed as the process capability measures for non-normal process.

## Citations

... Several univariate procedures have been properly extended to monitor multiple quality characteristics of a normally distributed process (see, for example, Cheng and Mao, 5 Khoo, and 6 Thaga and Gabaitiri 7 ). For additional multivariate CCs with memory, the interested reader is referred to the articles of Xie, 8 Cheng and Thaga, 9 Jeong and Cho, 10 and Chen et al. 11 When the question comes to the simultaneous surveillance of both mean and variance, a popular approach is to use two distinct statistics, one for the mean and one for the variance, which are plotted on the same chart (Spiring and Cheng 12 ); another approach, suggested by Cheng and Mao, 5 makes use of a single combined plotting statistic. Alternatively, the use of two-chart monitoring schemes has been suggested, which consist of separate mean and variance charts with appropriate control limits (CLs) adjusted to the overall false alarm rate FAR (see, for example, Levinson et al, 13 Reynolds and Stoumbos, 14 and Maboudou-Tchao and Hawkins 15 ). ...

... Mahmoud and Maravelakis (2013) explained the performance of multivariate CUSUM control charts with estimated parameters. Choi and Cho (2016) estimated the multivariate CUSUM control charts for monitoring the covariance matrix. Qiu and Hawkins (2001) have explained a rank-based multivariate CUSUM procedure. ...

... For both structures, M 1i is an identity matrix, while M 2i is a matrix with 0 on the diagonal and 1 elsewhere for exchangeable, and 1 on the sub-diagonal and 0 elsewhere for AR-1. Zhou et al., 7 and similarly Cho and Dashnyam, 15 modified M 2i , denoted as M Ã 2i , to be a lower triangular matrix for a Type II timedependent covariate, and thus TðT þ 1Þ=2 estimating equations for s ! j are used in g 2i ðbÞ. ...

... The purpose of the experiment was to learn how construct control charts and determine if a certain process is "in control" or not. Both X and R charts can be analyzed to determine whether measurement variations are random fluctuations, or indicative of a problem in the process (Cho, 2010). Control charts are widely used in manufacturing processes to determine if equipment failures or other problems are affecting the quality of the product (Ko and Cho, 2008). ...

... Chang and Cho (2005) studied CUSUM charts for monitoring mean vector with variable sampling intervals. Reynolds and Cho (2006), Reynolds and Cho (2011), Jeong and Cho (2012) studied multivariate control charts for the mean vector or covariance matrix. ...

... Cui and Reynolds (1988) considered VSI Shewhart X-chart with runs rules using Markov chain method and Markov chain method for multivariate EWMA chart can be referred from Chang et al. (2003). Multivariate process control procedures were studied by Jeong and Cho (2012) and Park and Cho (2013). ...

... They showed through numerical results that accumulate-combine method is more efficient than combine-accumulate method only in terms of ARL. Multivariate control charts with variable sampling interval (VSI) scheme were studied by Cho (2010), Im and Cho (2009) and Chang and Heo (2010). ...

... Moreover, because of the fact that QIF does not need to estimate the parameters in a given Yang et al. 95: 39 Medicine correlation structure, especially when the working correlation is misspecified, the QIF estimator of beta is more efficient than the GEE estimator. [30,33,34] Finally, GEE is not robust and very sensitive to influential data cases, whereas the QIF estimators are robust with a bounded influence function against unduly large outliers or contaminated data points. [30,35,36] The original continuous platelet indices were categorized into 4 levels (Q 1 , Q 2 , Q 3 , and Q 4 ) using the 3 quartiles of P 25 , P 50 , and P 75 as a critical values, with P 25 for Q 1 , >P 25 and P 50 for Q 2 , >P 50 and P 75 for Q 3 , and >P 75 for Q 4 , respectively. ...

... For researches in which single multivariate charts were used, one may refer to Khoo (2005), Zhang, Li, and Wang (2010), Wang, Yeh, and Li (2014) and Sabahno, Amiri, and Castagliola (2021). Concerning using adaptive features in simultaneous monitoring schemes, see Reynolds and Kim (2007), Reynolds and Cho (2011), andAmiri (2020a, 2020b) and Sabahno, Amiri, and Castagliola (2021). ...

... In general, control charts for simultaneous monitoring can be divided into single-chart and double-chart (one for each parameter) schemes. Concerning multivariate control charts, in which two separate charts were used, one may refer to Reynolds and Cho (2006), Hawkins and Maboudou-Tchao (2008), and Zhang and Chang (2008). For researches in which single multivariate charts were used, one may refer to Khoo (2005), Zhang, Li, and Wang (2010), Wang, Yeh, and Li (2014) and Sabahno, Amiri, and Castagliola (2021). ...