English  |  正體中文  |  简体中文  |  Items with full text/Total items : 57505/91036 (63%)
Visitors : 13424713      Online Users : 287
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/18487

    Title: Conditional Maximum Likelihood Estimation for Control Charts in the Presence of Correlation
    Authors: Tsai, Tzong-ru;Chiang, Y. C.;Wu, Shuo-jye
    Contributors: 淡江大學統計學系
    Keywords: Autoregressive moving average model;exponentially weighted moving average control charts;first-order autoregressive model;maximum likelihood estimation;Shewhart control chart
    Date: 2004-12-01
    Issue Date: 2009-09-01 16:08:53 (UTC+8)
    Publisher: Sao Paulo: Associacao Brasileira de Estatistica
    Abstract: In practice, the observations are usually autocorrelated. The autocorrelation between successive observations has a large impact on control charts with the assumption of independence. It can decrease the in-control average run length which leads to a higher false alarm rate than in the case of independent process. This paper considers the problem of monitoring the mean of AR(1) process with a random error and provides a conditional maximum likelihood estimation method to improve the control chart performance when the sample size is small. Numerical result shows that the standard estimation method is very unstable when the sample size is small, and there is a large probability that the standard estimation method breaks down if the level of correlation between successive means is small-to-moderate. The new method given here overcomes this difficulty.
    This paper proposes a heuristic method based on adjusted weighted standard deviation for constructing R chart for skewed process distributions. The asymmetric control limits of the chart arc established with no assumption to the process distribution. If the process distribution is symmetric those control limits are equivalent to those of Shewhart R chart. The proposed control limits are compared with weighted variance R chart and skewness correction R chart by Monte Carlo simulation. When the process distribution is Weibull or gamma, simulation results show that the proposed R chart performs better than both weighted variance and skewness correction R chart as the skewness and the sample size increase. For the case where the process distribution is exponential with known mean the Type I risk and Type II risk of the proposed R chart are closer to those of the exact R chart than those of the weighted variance and skewness correction R charts.
    Relation: Brazilian Journal of Probability and Statistics 18(2), pp.151-162
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

    Files in This Item:

    File Description SizeFormat
    0103-0752_18(2)p151-162.pdf152KbAdobe PDF449View/Open
    Conditional Maximum Likelihood Estimation for Control Charts in the Presence of Correlation.pdf152KbAdobe PDF0View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback