Lower Confidence Bounds for One-Sided Process Capability Index CpuT with Multiple Characteristics
碩士 === 國立交通大學 === 工業工程與管理學系 === 100 === Process capability indices (PCIs) for processes with a single quality characteristic have been investigated extensively. For processes involved multiple characteristics, we consider the one-sided process capability index CpuT. However, the exact sampling distr...
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ndltd-TW-100NCTU50310712016-03-28T04:20:36Z http://ndltd.ncl.edu.tw/handle/72590669841374054712 Lower Confidence Bounds for One-Sided Process Capability Index CpuT with Multiple Characteristics 單邊規格多品質特性製程能力指標CpuT的信賴下限 Wei, Yu-Hsuan 魏瑜萱 碩士 國立交通大學 工業工程與管理學系 100 Process capability indices (PCIs) for processes with a single quality characteristic have been investigated extensively. For processes involved multiple characteristics, we consider the one-sided process capability index CpuT. However, the exact sampling distribution of CpuT is mathematically intractable. In this research, in order to make inferences on the process capability, we apply an asymptotic normal distribution for the natural estimator of CpuT and investigate the accuracy with the corresponding sample size by simulation. In addition, depending on the sample size, a performance comparison among the normal approximate method and four kinds of the bootstrap approach for 95% lower confidence bounds is conducted based on coverage rate and mean of the lower confidence bounds. Finally, an application from manufacturing process with multiple characteristics is presented. In general, the result indicates that the normal approximate technique is more reliable than the four bootstrap methods. Pearn, W. L. 彭文理 2012 學位論文 ; thesis 55 en_US |
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碩士 === 國立交通大學 === 工業工程與管理學系 === 100 === Process capability indices (PCIs) for processes with a single quality characteristic have been investigated extensively. For processes involved multiple characteristics, we consider the one-sided process capability index CpuT. However, the exact sampling distribution of CpuT is mathematically intractable. In this research, in order to make inferences on the process capability, we apply an asymptotic normal distribution for the natural estimator of CpuT and investigate the accuracy with the corresponding sample size by simulation. In addition, depending on the sample size, a performance comparison among the normal approximate method and four kinds of the bootstrap approach for 95% lower confidence bounds is conducted based on coverage rate and mean of the lower confidence bounds. Finally, an application from manufacturing process with multiple characteristics is presented. In general, the result indicates that the normal approximate technique is more reliable than the four bootstrap methods.
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author2 |
Pearn, W. L. |
author_facet |
Pearn, W. L. Wei, Yu-Hsuan 魏瑜萱 |
author |
Wei, Yu-Hsuan 魏瑜萱 |
spellingShingle |
Wei, Yu-Hsuan 魏瑜萱 Lower Confidence Bounds for One-Sided Process Capability Index CpuT with Multiple Characteristics |
author_sort |
Wei, Yu-Hsuan |
title |
Lower Confidence Bounds for One-Sided Process Capability Index CpuT with Multiple Characteristics |
title_short |
Lower Confidence Bounds for One-Sided Process Capability Index CpuT with Multiple Characteristics |
title_full |
Lower Confidence Bounds for One-Sided Process Capability Index CpuT with Multiple Characteristics |
title_fullStr |
Lower Confidence Bounds for One-Sided Process Capability Index CpuT with Multiple Characteristics |
title_full_unstemmed |
Lower Confidence Bounds for One-Sided Process Capability Index CpuT with Multiple Characteristics |
title_sort |
lower confidence bounds for one-sided process capability index cput with multiple characteristics |
publishDate |
2012 |
url |
http://ndltd.ncl.edu.tw/handle/72590669841374054712 |
work_keys_str_mv |
AT weiyuhsuan lowerconfidenceboundsforonesidedprocesscapabilityindexcputwithmultiplecharacteristics AT wèiyúxuān lowerconfidenceboundsforonesidedprocesscapabilityindexcputwithmultiplecharacteristics AT weiyuhsuan dānbiānguīgéduōpǐnzhìtèxìngzhìchéngnénglìzhǐbiāocputdexìnlàixiàxiàn AT wèiyúxuān dānbiānguīgéduōpǐnzhìtèxìngzhìchéngnénglìzhǐbiāocputdexìnlàixiàxiàn |
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