### 博碩士論文 100225021 完整後設資料紀錄

 DC 欄位 值 語言 DC.contributor 統計研究所 zh_TW DC.creator 龍庭軒 zh_TW DC.creator Ting-Hsuan Long en_US dc.date.accessioned 2013-6-19T07:39:07Z dc.date.available 2013-6-19T07:39:07Z dc.date.issued 2013 dc.identifier.uri http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=100225021 dc.contributor.department 統計研究所 zh_TW DC.description 國立中央大學 zh_TW DC.description National Central University en_US dc.description.abstract 不論在工業方面或是商業方面而言，統計製程管制(Statistical process control)皆是一個非常重要的品質管制工具。傳統的Shewhart管制圖僅是運用在獨立性的假設之下。然而，在現實生活中，存在著許多相關性假設的資料，因此，傳統的管制圖在現實生活中是不被接受且不實用的。在本文，我們主要的目的是以建立在copula之下的馬可夫鏈模型去衍伸我們的相關性假設資料。此外，我們提出了最大概似估計量的方法估計我們的未知參數，分別為管制上限(UCL)以及管制下限(LCL)。接著，我們使用蒙地卡羅模擬法做出平均串聯長度(Average run length)以用來表現管制圖的性質。最後，我們提出了降低變異數的方法去增加資料的準確性。 zh_TW dc.description.abstract Statistical process control is an important and convenient tool for business and industry. The traditional Shewhart control chart has been a popular tool for process control, which however is valid under the independence assumption of consecutive observations. In real world applications, there exist many types of dependent observations in which the traditional control charts cannot be used. In this paper, we apply a copula-based Markov chain to model the correlated observations. In particular, we proposed a maximum likelihood method to obtain the estimates of upper control limit (UCL) and lower control limit (LCL). It is shown by simulations that the proposed method provide more accurate estimates of the UCL and LCL than the existing procedure and traditional procedure. We also consider Monte Carlo simulations to compute the value of the average run length (ARL) of the proposed charts. Here, we suggest a variance reduction technique, called antithetic variables method to gain computational efficiency. Two datasets are analyzed for illustration. en_US DC.subject 平均串聯長度 zh_TW DC.subject Copula zh_TW DC.subject 相關性資料 zh_TW DC.subject 馬可夫鏈 zh_TW DC.subject 降低變異數 zh_TW DC.subject Average run length en_US DC.subject Copula en_US DC.subject correlated data en_US DC.subject Markov chain en_US DC.subject variance reduction en_US DC.title A control chart based on copula-based Markov time series models en_US dc.language.iso en_US en_US DC.type 博碩士論文 zh_TW DC.type thesis en_US DC.publisher National Central University en_US