• Journal of Korean Society for Quality Management
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• v.30 no.2
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• pp.152-159
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• 2002

An acceptance sampling plan for products manufactured from a production process with variable fraction defective is developed. We consider a situation where defective products have short lifetimes and non-defective ones never fail during the technological life of the products. An acceptance criterion which guarantee the out going quality of accepted products is derived using the prior information on the quality of products. Numerical examples are provided.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.387-402
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• 2009

In this paper we have considered the problem of estimating the population mean $\bar{Y}$ of the study variable y using auxiliary information in presence of non-response. Classes of estimators for $\bar{Y}$ in the presence of non-response on the study variable y only and complete response on the auxiliary variable x is available, have been proposed in different situations viz., (i) population mean $\bar{X}$ is known, (ii) when population mean $\bar{X}$ and variance $S^2_x$ are known; (iii) when population mean $\bar{X}$ is not known: and (iv) when both population mean $\bar{X}$ and variance $S^2_x$ are not known: single and two-phase (or double) sampling. It has been shown that various estimators including usual unbiased estimator and the estimators reported by Rao (1986), Khare and Srivastava (1993, 1995) and Tabasum and Khan (2006) are members of the proposed classes of estimators. The optimum values of the first phase sample size n', second phase sample size n and the sub sampling fraction 1/k have been obtained for the fixed cost and the fixed precision. To illustrate foregoing, we have carried out an empirical investigation to reflect the relative performance of all the potentially competing estimators including the one due to Hansen and Hurwitz (1946) estimator, Rao (1986) estimator, Khare and Srivastava (1993, 1995) and Tabasum and Khan (2006) estimator.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1247-1251
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• 2016

This study aims to investigate the effect of sampling frequency to the time domain and frequency domain analysis of pulse rate variability (PRV). Typical time domain variables - AVNN, SDNN, SDSD, RMSSD, NN50 count and pNN50 - and frequency domain variables - VLF, LF, HF, LF/HF, Total Power, nLF and nHF - were derived from 7 down-sampled (250 Hz, 100 Hz, 50 Hz, 25 Hz, 20 Hz, 15 Hz, 10 Hz) PRVs and compared with the result of heart rate variability of 10 kHz-sampled electrocardiogram. Result showed that every variable of time domain analysis of PRV was significant at 25 Hz or higher sampling frequency. Also, in frequency domain analysis, every variable of PRV was significant at 15 Hz or higher sampling frequency.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.457-468
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• 2007

This paper proposes an adaptive moving average (A-MA) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI A-MA chart is to adjust sampling intervals as well as to accumulate previous samples selectively in order to increase the sensitivity. The VSI A-MA chart employs a threshold limit to determine whether or not to increase sampling rate as well as to accumulate previous samples. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI A-MA chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The control length L is introduced to prevent small mean shifts from being undetected for a long period. A Markov chain model is employed to investigate the VSI A-MA sampling process. Formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI A-MA chart is proposed. Comparative studies show that the proposed VSI A-MA chart is uniformly superior to the adaptive Cumulative sum (CUSUM) chart and to the Exponentially Weighted Moving Average (EWMA) chart, and is comparable to the variable sampling size (VSS) VSI EWMA chart with respect to the ATS performance.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.993-1004
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• 2017

A stratified sampling method is generally used with a sample selected using the same sample weight in each stratum in order to improve the accuracy of the sampling survey estimation. However, the weight should be adjusted to reflect the response rate if the response rate is affected by the value of the variable of interest. It may be also more effective to adjust the weights by subdividing the stratum rather than using the same weight if the variable of interest has a linear relationship with the continuous auxiliary variables. In this study, we propose a method to increase the accuracy of estimation using an informative sampling design technique when the response rate is an exponential function of the variable of interest and the variable of interest has a linear relationship with the auxiliary variable. Simulation results show the superiority of the proposed method.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.1
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• pp.1-12
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• 2014

This article proposes a variable sampling interval (VSI) $\bar{X}$ control chart using weighted standard deviation (WSD) method for skewed populations. The WSD method decomposes the standard deviation of a quality characteristic into upper and lower deviations and adjusts control limits and warning limits of a control chart in accordance with the direction and degree of skewness. A control chart constant is derived for estimating the standard deviation of skewed distributions with the mean of sample standard deviations. The proposed chart is compared with the conventional VSI $\bar{X}$ control chart under some skewed distributions. Simulation study shows that the proposed WSD VSI chart can control the in-control average time to signal (ATS) as an adequate level better than the conventional VSI chart, and the proposed chart can detect a decrease in the process mean of a quality characteristic following a positively skewed distribution more quickly than the standard VSI chart.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.359-364
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• 2002

In case of pn control chart often used in mass production system of plant industry and so on, we could evaluate it's performance by the approximation to normal distribution. It has many differences according to sample sizes and defective fraction, and have disadvantage that needs much samples to use the normal distribution approximation. Existent control charts can not detect the cause of process something wrong because it is taking the sampling intervals of fixed length about all times from the process. Therefore, to overcome this shortcoming we use VSI(variable sampling intervals) techniques in this paper. This technique takes a long sampling interval to have the next sampling point if the sample point is in stable state, and if the sample point is near control lines, it takes short sampling interval because the probability to escape control limit is high. To analyze performance of pn control charts that have existent fixed sampling intervals(FSI) and that use VSI technique, we compare ATS of two charts, and analyze the performance of each control chart by the sample sizes, process fraction defective and control limits that Ryan and Schwertman had proposed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.560-570
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• 2006

This paper proposes a selectively cumulative sum (S-CUSUM) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI S-CUSUM chart is to adjust sampling intervals and to accumulate previous samples selectively in order to increase the sensitivity. The VSI S-CUSUM chart employs a threshold limit to determine whether to increase sampling rate as well as to accumulate previous samples or not. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI S-CUSUM chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The number L is a decision variable and is called a 'control length'. A Markov chain model is employed to describe the VSI S-CUSUM sampling process. Some useful formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI S-CUSUM chart is proposed. Comparative studies show that the proposed VSI S-CUSUM chart is uniformly superior to the VSI CUSUM chart or to the Exponentially Weighted Moving Average (EWMA) chart with respect to the ATS performance.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.999-1008
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• 2010

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 the problem of simultaneously monitoring both the mean and variability of the process.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.107-116
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• 1999

We propose a sample design which minimize Bayes risk for cluster smpling in sampling inspection. We treat a pilot sample and an additional sample size as random variable. In addition we compute an appropriate cluster size for handling over-dispersion.