English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 112704/143671 (78%)
Visitors : 49721882      Online Users : 713
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU 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
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/153360
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153360


    Title: 多項比例精確管制圖之研究
    Study of exact control chart for monitoring multinomial distribution processes
    Authors: 甘勝進
    Gan, Sheng-Jin
    Contributors: 楊素芬
    陳立榜

    Yang Su-Fen
    Chen Li-Pang

    甘勝進
    Gan, Sheng-Jin
    Keywords: 多項分配過程
    皮爾森卡方統計量
    加權指數滑動平均
    測量誤差
    measurement error
    Multinomial distribution process
    Pearson’s Chi-square statistic
    EWMA
    Date: 2024
    Issue Date: 2024-09-04 14:55:34 (UTC+8)
    Abstract: 管制圖已經廣汎應用于製造業中的質量監控, 在過程質量發生改變時及時發出報警方面,它扮演著重要的角色. 現有管制圖主要側重單變量或多變量連續型過程分配.爲了處理離散型分配,特別是多項分配過程, 借助皮爾遜卡方統計量來構建管制圖可能是一個共同的選擇. 然而, 這種管制圖嚴重依賴大樣本, 當樣本容量較小或者中等時產生不可靠結果. 本論文中, 我們主要探索多項分配過程管制圖. 我們首先重新審視了皮爾森卡方統計量,并推導出了其任意樣本下的均值和方差. 然後, 建立精確的EWMA比例管制圖. 與現有基於符號的EWMA管制圖和多項CUSUM圖相比, 模擬結果證明了我們方法的檢測性能. 另外, 測量誤差對精確管制圖的影響也得到研究, 一些模擬表明測量誤差延緩失控信號的發出.
    Control charts have been widely used for monitoring output quality in manufacturing. It plays an important role in triggering a signal in time when detecting a change in process quality. Most existing control charts focus on the univariate or multivariate process data with continuous distribution. To deal with discrete distributions, in particular, the multinomial distribution processes, Pearson’s Chi-square statistic might be a common approach to construct control charts. However, it depends heavily on large sample sizes, which can yield unreliable result when sample size is small or moderate. In this thesis, we primarily explore the process control chart for multinomial distribution data. We first review Pearson’s Chi-square statistic, and derive the exact mean and variance regardless of sample sizes. After that, the exact exponentially weighted moving average (EWMA) proportions chart is derived under small or large sample sizes. Compared with existing sign-based EWMA chart and multinomial CUSUM chart for monitoring the multinomial distribution processes, simulation study is conducted to assess the performance of our proposed chart. Moreover, affection of measurement error on the exact control chart is also investigated, some simulation results suggest that measurement error delay detecting in out-of -control processes.
    Reference: Abbasi, S. A. (2010). On the performance of EWMA chart in the presence of two-component measurement error. Quality Engineering, 22(3), 199-213.
    Chandrasekaran, S., English, J. R., & Disney, R. L. (1995). Modeling and analysis of EWMA control schemes with variance-adjusted control limits. IIE Transactions, 27(3), 282-290.
    Chen, L. P., & Yang, S. F. (2023). A new p-control chart with measurement error correction. Quality and Reliability Engineering International, 39(1), 81-98.
    Cocchi, D., & Scagliarini, M. (2011). Effects of the two-component measurement error model on X control charts. Statistica, 71(3), 307-327.
    Crosier, R. B. (1988). Multivariate generalizations of cumulative sum quality-control schemes. Technometrics, 30(3), 291-303.
    Falk, M. (1999). A simple approach to the generation of uniformly distributed random variables with prescribed correlations. Communications in Statistics-Simulation and Computation, 28(3), 785-791.
    Gan, S., & Yang, S. F.(2020). A EWMA control chart for multinomial distribution with application to monitoring multivariate mean shift. Technical report, National ChengChi University.
    Gan, S., Yang, S. F., & Chen, L. P. (2023). A new EWMA control chart for monitoring multinomial proportions. Sustainability, 15(15), 11797.
    Huang, W., Reynolds Jr, M. R., & Wang, S. (2012). A binomial GLR control chart for monitoring a proportion. Journal of Quality Technology, 44(3), 192-208.
    Huang, W., Wang, S., & Reynolds Jr, M. R. (2013). A generalized likelihood ratio chart for monitoring Bernoulli processes. Quality and Reliability Engineering International, 29(5), 665-679.
    Lee, J., Peng, Y., Wang, N., & Reynolds Jr, M. R. (2017). A GLR control chart for monitoring a multinomial process. Quality and Reliability Engineering International, 33(8), 1773-1782.
    Li, J., Tsung, F., & Zou, C. (2014). Multivariate binomial/multinomial control chart. IIE Transactions, 46(5), 526-542.
    Linna, K. W., & Woodall, W. H. (2001). Effect of measurement error on Shewhart control charts. Journal of Quality Technology, 33(2), 213-222.
    Linna, K. W., Woodall, W. H., & Busby, K. L. (2001). The performance of multivariate control charts in the presence of measurement error. Journal of Quality Technology, 33(3), 349-355.
    Lucas, J. M., & Saccucci, M. S. (1990). Exponentially weighted moving average control schemes: properties and enhancements. Technometrics, 32(1), 1-12.
    Maravelakis, P. E. (2012). Measurement error effect on the CUSUM control chart. Journal of Applied Statistics, 39(2), 323-336.
    Maravelakis, P., Panaretos, J., & Psarakis, S. (2004). EWMA chart and measurement error. Journal of Applied Statistics, 31(4), 445-455.
    Marcucci, M. (1985). Monitoring multinomial processes. Journal of Quality Technology, 17(2), 86-91.
    Montgomery, D. C. (2009). Introduction to Statistical Quality Control. John Wiley & Sons.
    Nelson, L.S.(1987). A chi-square control chart for several proportions. Journal of Quality Technology, 19(4), 229-231.
    Qiu, P. (2008). Distribution-free multivariate process control based on log-linear modeling. IIE Transactions, 40(7), 664-677.
    Qiu, P. (2013). Introduction to Statistical Process Control. CRC press.
    Reynolds, M. R., & Stoumbos, Z. G. (1998). The SPRT chart for monitoring a proportion. IIE Transactions, 30(6), 545-561.
    Reynolds, M. R., & Stoumbos, Z. G. (2001). Monitoring a proportion using CUSUM and SPRT control charts. In Frontiers in Statistical Quality Control 6 (pp.155-175). Physica, Heidelberg.
    Ryan, A. G., Wells, L. J., & Woodall, W. H. (2011). Methods for monitoring multiple proportions when inspecting continuously. Journal of Quality Technology, 43(3), 237-248.
    Woodall, W. H. (1997). Control charts based on attribute data: bibliography and review. Journal of Quality Technology, 29(2), 172-183.
    Description: 博士
    國立政治大學
    統計學系
    108354502
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108354502
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File SizeFormat
    450201.pdf602KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback