• 대한산업공학회지
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• 제23권4호
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• pp.755-767
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• 1997

Analyzing autocorrelated data set is still an open problem. Developing on easy and efficient method for severe positive correlated data set, which is common in simulation output, is vital for the simulation society. Bootstrap is on easy and powerful tool for constructing non-parametric inferential procedures in modern statistical data analysis. Conventional bootstrap algorithm requires iid assumption in the original data set. Proper choice of resampling units for generating replicates has much to do with the structure of the original data set, iid data or autocorrelated. In this paper, a new bootstrap resampling scheme is proposed to analyze the autocorrelated data set : the Threshold Bootstrap. A thorough literature search of bootstrap method focusing on the case of autocorrelated data set is also provided. Theoretical foundations of Threshold Bootstrap is studied and compared with other leading bootstrap sampling techniques for autocorrelated data sets. The performance of TB is reported using M/M/1 queueing model, else the comparison of other resampling techniques of ARMA data set is also reported.

• 품질경영학회지
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• 제27권4호
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• pp.123-142
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• 1999

Conventional SPC assumes that observations are independent. Often in industrial practice, however, observations are not independent. A common approach to building control charts for autocorrelated data is to apply conventional SPC to the residuals from a time series model of the process or is to apply conventional SPC to the weighted or unweighted subgroup means. In this paper, we propose a robust CUSUM control scheme for the detection of level change, without model identification or subgrouping of autocorrelated data. The proposed CUSUM chart and other conventional control charts are compared by a Monte Carlo simulation. It is shown that the proposed CUSUM chart is more effective than conventional CUSUM chart when the process is autocorrelated.

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.33-38
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• 1994

The OLS-estimator of the distribance variance in the linear regression model is shown to be asymptotically unbiased when the disturbances are MA(1)-process or particular s-th autocorrelated AR(s)-process.

• 응용통계연구
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• 제28권5호
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• pp.1025-1034
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• 2015

1920년대에 소개되었던 Shewhart 관리도는 관측치가 서로 독립임을 가정했다. 오늘날은 데이터 측정과 자료수집 기술이 발전하면서 자기상관 공정 데이터가 많이 발생하고 있으며, 이것은 통계적 공정 관리의 성능에 부정적인 영향을 끼치게 된다. 자기상관이 존재하는 데이터에 대하여 가장 쉽게 접근할 수 있는 관리도는 먼저 자기상관구조를 모형화할 수 있는 적절한 시계열 모형을 가정한 다음 잔차를 구하여, 그 잔차에 기반한 Shewhart 관리도를 적용하는 것이다. 실제 문제에서 시계열 모형의 참 모수값은 알려져 있지 않으므로, 이 값은 일단계 표본(과거의 관리상태 표본)으로부터 추정된다. 본 논문에서는 이러한 모수추정이 이단계 표본을 모니터링하는데 어떠한 영향이 있는지 살펴보았다.

• Asia Marketing Journal
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• 제11권1호
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• pp.29-38
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• 2009

시계열 확산 데이터를 활용하여 Bass 확산모형을 최소자승법(OLS)으로 추정하면, 초기에는 과다 추정하고 변곡점을 지나서는 수요를 낮게 추정하는 경향이 있다. 또한 확산모형에서 필요한 변수가 모형에서 빠짐으로 인해 발생하는 설정오류는 잔차의 자기상관을 발생시킬 수 있다. 자기상관이 오차항에 있을 경우, 추정된 모형의 모수들은 불편추정치이나 비효율적 추정치가 된다. 따라서 이러한 문제를 해결하는 확산모형의 개발이 요구된다. 본 연구에서는 자기상관 오차항을 고려한 수정된 확산모형을 제안하였다. 모형의 검증을 위해 미국의 CT-스캐너와 우리나라의 FPD TV 판매량를 제안된 모형에 응용하였다. 분석결과, 제안된 모형이 기존 모형에 비해 적합도와 모형의 주요 추정 통계량에서 우수함을 보였다.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.289-301
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• 2003

In this paper, we propose Shewhart control chart and EWMA control chart using the autocorrelated data which are common in chemical and process industries and lead to increase the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and simulation is conducted to investigate the performances of the proposed control charts.

• 대한산업공학회지
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• 제34권2호
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• pp.122-139
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• 2008

With the proliferation of in-process measurement technology, autocorrelated data are increasingly common in industrial SPC applications. A number of high performance control charting techniques that take into account the specific characteristics of the autocorrelation through time series modeling have been proposed over the past decade. We present a survey of such methods and analyze and compare their performances for a range of typical autocorrelated process models. One practical concern with these methods is that their performances are often strongly affected by errors in the time series models used to represent the autocorrelation. We also provide some analytical results comparing the robustness of the various methods with respect to time series modeling errors.

• 대한산업공학회지
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• 제27권3호
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• pp.289-296
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• 2001

This paper proposes multivariate control chart for autocorrelated data which are common in chemical and process industries and lead to increase in the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a vector autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and therefore the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and Monte Carlo simulation is conducted to investigate the performances of the proposed control charts.

• 한국국방경영분석학회지
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• 제8권1호
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• pp.71-76
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• 1982

A procedure is established for combining a regression model and a time series model to fit to a set of autocorrelated data. This procedure is based on an iterative method to compute regression parameter estimates and time series parameter estimates simultaneously. The time series model which is discussed is basically AR(p) model, since MA(q) model or ARMA(p,q) model can be inverted to AR({$\infty$) model which can be approximated by AR(p) model. The procedure discussed in this articled is applied in general to any combination of regression model and time series model.

• Journal of the Korean Data and Information Science Society
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• 제13권1호
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• pp.105-112
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• 2002

In this paper we establish bivariate exponentially weighted moving average (EWMA) control charts for autocorrelated processes using residual vectors. We first derive the residual vectors, their expectation, variance-covariance matrix, then evaluate the control chart based on the average run length (ARL).