• Title/Summary/Keyword: change point process

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Estimation of Change Point in Process State on CUSUM ($\bar{x}$, s) Control Chart

  • Takemoto, Yasuhiko;Arizono, Ikuo
    • Industrial Engineering and Management Systems
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    • v.8 no.3
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    • pp.139-147
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    • 2009
  • Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.

Comparison of Change-point Estimators in Hazard Rate Models

  • Kim, Jaehee
    • Communications for Statistical Applications and Methods
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    • v.9 no.3
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    • pp.753-763
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    • 2002
  • When there is one change-point in the hazard rate model, a change-point estimator with the partial score process is suggested and compared with the previously developed estimators. The limiting distribution of the partial score process we used is a function of the Brownian bridge. Simulation study gives the comparison of change-point estimators.

Test and Estimation for Exponential Mean Change

  • Kim, Jae-Hee
    • Communications for Statistical Applications and Methods
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    • v.15 no.3
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    • pp.421-427
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    • 2008
  • This paper deals with the problem of testing for the existence of change in mean and estimating the change-point when the data are from the exponential distributions. The likelihood ratio test statistic and Gombay and Horvath (1990) test statistic are compared in a power study when there exists one change-point in the exponential means. Also the change-point estimator using the likelihood ratio and the change-point estimators based on Gombay and Horvath (1990) statistic are compared for their detecting capability via simulation.

A Bayesian Inference for Power Law Process with a Single Change Point

  • Kim, Kiwoong;Inkwon Yeo;Sinsup Cho;Kim, Jae-Joo
    • International Journal of Quality Innovation
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    • v.5 no.1
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    • pp.1-9
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    • 2004
  • The nonhomogeneous poisson process (NHPP) is often used to model repairable systems that are subject to a minimal repair strategy, with negligible repair times. In this situation, the system can be characterized by its intensity function. There have been many NHPP models according to intensity functions. However, the intensity function of system in use can be changed because of repair or its aging. We consider the single change point model as the modification of the power law process. The shape parameter of its intensity function is changed before and after the change point. We detect the presence of the change point using Bayesian methodology. Some numerical results are also presented.

Estimation of the Change Point in VSS X Control Charts

  • Lee, Jaeheon;Park, Changsoon
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.825-833
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    • 2003
  • Knowing the time of the process change could lead to quicker identification of the responsible special cause and less process down time, and it could help to reduce the probability of incorrectly identifying the special cause. In this paper, we propose a maximum likelihood estimator of the process change point when a Shewhart $\bar{X}$ chart with variable sample size (VSS) scheme signals a change in the process mean. Also we build a confidence interval for the process change point by using the likelihood function.

A change point estimator in monitoring the parameters of a multivariate IMA(1, 1) model

  • Sohn, Sun-Yoel;Cho, Gyo-Young
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.2
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    • pp.525-533
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    • 2015
  • 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 more easily. To know it, we derive a maximum likelihood estimator (MLE) of the change point in a process when observations are from a multivariate IMA(1,1) process by monitoring residual vectors of the model. In this paper, numerical results show that the MLE of change point is effective in detecting changes in a process.

Comparison of Change-point Estimators with Scores

  • Kim, Jae-Hee;Seo, Hyun-Joo
    • Journal of the Korean Data and Information Science Society
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    • v.13 no.1
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    • pp.165-175
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    • 2002
  • We consider the problem of estimating the change-point in mean change model with the one change-point. Lombard (1987) suggested change-point estimation based on score functions. Gombay and Huskova (1998) derived a class of change-point estimators with the score function of rank. Various change-point estimators with the log score functions of ranks are suggested and compared via simulation.

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An On-Line Real-Time SPC Scheme and Its Performance

  • Nishina, Ken
    • International Journal of Quality Innovation
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    • v.2 no.1
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    • pp.30-49
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    • 2001
  • This paper considers a recent environment in the manufacturing process in which data in large amounts can be obtained on-line in real-time. Under this environment an on-line real-time Statistical Process Control (SPC) scheme equipped with detection of a process change, change-point estimation, and recognition of the change pattern is proposed. The proposed SPC scheme is composed of a Cusum chart, filtering methods and Akaike Information Criterion (AIC). We examine the performance of this scheme by Monte Carlo simulation and show its usefulness.

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Asymptotic Properties of Variance Change-point in the Long-memory Process

  • Chu Minjeong;Cho Sinsup
    • Proceedings of the Korean Statistical Society Conference
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    • 2000.11a
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    • pp.23-26
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    • 2000
  • It is noted that many econometric time series have long-memory properties. A long-memory process, or strongly dependent process, is characterized by hyperbolic decaying autocorrelations and unbounded spectral density at the origin. Since the long-memory property can be observed by data obtained from rather a long period, there is some possibility of parameter change in the process. In this paper, we consider the estimation of change-point when there is a change in the variance of a long-memory process. The estimator is based on some reasonable statistic and the consistency is shown using Taqqu's strong reduction theorem

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Efficient Auto Measure Sampling Method for Semiconductor Line (반도체 라인의 효율적 계측을 위한 자동 계측 샘플링 방식에 관한 연구)

  • Kim, Tae-Yeob;Sun, Dong-Seok;Lee, Jee-Hyung
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.12
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    • pp.2505-2510
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    • 2009
  • Semiconductor processes need measurement to confirm where there are problems in quality after progresses manufacturing process. This paper suggests equipment and automatic measure sampling method that control monitoring ratio according to change point occurrence availability of process that is not measure method by the existent simple ratio rate. This paper defines measure section as ailment section, metastable section and stability section by change point standard and create statistical model of each section and developed suitable measure rate model by section. As a result, we have accomplished maximum throughput and minimum sampling number that needs to maintain constant level of quality. Proposed method minimizes load of measure process by brings production quality sophistication and decrease of process badness and lowers measure rate in stable section making perception about problem occurrence quick heightening measure rate at change point occurrence.