Monitoring process data with seasonal time series model

碩士 === 國立政治大學 === 統計研究所 === 101 === Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed. However, the independence assumption is often violated in practice. Autocorrelation may be represented in many proces...

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Main Author: 王儀茹
Other Authors: 楊素芬
Format: Others
Language:en_US
Online Access:http://ndltd.ncl.edu.tw/handle/49031987940557216857
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spelling ndltd-TW-101NCCU53370222016-07-02T04:20:16Z http://ndltd.ncl.edu.tw/handle/49031987940557216857 Monitoring process data with seasonal time series model 追蹤季節性時間數列模型之流程資料 王儀茹 碩士 國立政治大學 統計研究所 101 Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed. However, the independence assumption is often violated in practice. Autocorrelation may be represented in many processes. To solve this problem, it is becoming more common to obtain profiles at each time period. Profile monitoring is the use of control charts for cases in which the quality of a process or product can be characterized by a functional relationship between a response variable and one or more explanatory variables. For the data with seasonal time series model, we propose several monitoring approaches to detect the out-of-control profiles. After considering both Phase I and Phase II schemes, a real example is given to illustrate the results. 楊素芬 鄭宗記 學位論文 ; thesis 64 en_US
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description 碩士 === 國立政治大學 === 統計研究所 === 101 === Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed. However, the independence assumption is often violated in practice. Autocorrelation may be represented in many processes. To solve this problem, it is becoming more common to obtain profiles at each time period. Profile monitoring is the use of control charts for cases in which the quality of a process or product can be characterized by a functional relationship between a response variable and one or more explanatory variables. For the data with seasonal time series model, we propose several monitoring approaches to detect the out-of-control profiles. After considering both Phase I and Phase II schemes, a real example is given to illustrate the results.
author2 楊素芬
author_facet 楊素芬
王儀茹
author 王儀茹
spellingShingle 王儀茹
Monitoring process data with seasonal time series model
author_sort 王儀茹
title Monitoring process data with seasonal time series model
title_short Monitoring process data with seasonal time series model
title_full Monitoring process data with seasonal time series model
title_fullStr Monitoring process data with seasonal time series model
title_full_unstemmed Monitoring process data with seasonal time series model
title_sort monitoring process data with seasonal time series model
url http://ndltd.ncl.edu.tw/handle/49031987940557216857
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