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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/60435


    Title: 無母數指數加權移動平均管制圖伴隨變動管制界限
    A Nonparametric EWMA-Type Signed-Rank Control Chart with Time-Varying Control Limits
    Authors: 鄭舜壕
    Contributors: 黃子銘
    鄭舜壕
    Keywords: 無母數管制圖
    指數加權移動平均
    平均連串長度
    Date: 2009
    Issue Date: 2013-09-05 15:11:19 (UTC+8)
    Abstract: 根據Steiner (1999) 提出指數加權移動平均 (EWMA) 管制圖之管制界限應伴隨時間變動，相較於傳統以漸近管制界限建構的 X-bar EWMA 管制圖，具備類似於快速起始反應之功能。然而，無母數EWMA 管制圖相關文獻中，大多採用漸近管制界限，甚少提及變動管制界限對於製程初期偵測能力之影響，因此本研究依據Wilcoxon 符號排序統計量為基礎，建構無母數EWMA 管制圖，並定義變動管制界限之形式，進而探討在製程初期的監控效果。假設製程為常態、均勻或雙指數分配下，使用非齊一性馬可夫鏈及蒙地卡羅模擬，求得製程穩定或失控狀態下的平均連串長度。模擬結果顯示，當加權常數越小，若採用變動管制界限能有效提升對於製程初期異常之偵測能力，且在厚尾分配下(例如：雙指數分配) 效果更為明顯。
    According to Steiner (1999), the control limits of exponentially weighted moving average (EWMA) control charts should vary with time, so that the charts would have properties similar to the fast initial response (FIR) feature, when compared with asymptotic X-bar EWMA charts. However, previous analyses of nonparametric EWMA control charts consider only asymptotic control limits and are not sensitive to the shifts in a process at early stages. In this thesis, a nonparametric control chart with time-varying control limits is constructed based on EWMA control chart built upon the Wilcoxon signed-rank statistics. When the underlying distribution is normal, uniform, or double exponential, the average run lengths in both in-control and out-of-control conditions are approximated using non-homogenous Markov chain and based on Monte Carlo simulations. Simulation results show that EWMA charts with varying control limits are more efficient to detect early process shifts when weighting constants are small, and the underlying distributions are heavy-tailed distribution (such as double exponential distribution).
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    Description: 碩士
    國立政治大學
    統計研究所
    97354020
    98
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0097354020
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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