當製造商的元件來自兩個供應商，且個別供應商的元件品質可能不一致時，在混合的比例已知的條件下，本論文採用元件壽命服從韋伯分配的假設，研究在逐步型一區間設限樣本之混合韋伯分配下的參數估計問題，使用 Metropolis-Hasting 馬可夫鏈蒙地卡羅演算法得到模型的參數估計的結果，並以蒙地卡羅模擬評估估計方法的成效。通過模擬結果得到在大樣本下，本論文提出的估計結果相對穩定。

When two suppliers supply components to a manufacturing company and the components from different suppliers could have different levels of quality, the mixed Weibull distributions is considered as the lifetime model of components. Moreover, an analytical Metropolis-Hasting Markov chain Monte Carlo procedure is proposed to estimate the model parameters. A simulation study is carried out to evaluate the performance of the proposed estimation method. Simulation results show that the proposed estimation method perform well with large samples.

目錄
中文摘要	I
Abstract	II
目錄	III
圖目錄	IV
表目錄	V
第一章 緒論	1
第二章 統計方法	7
  2.1 統計模型	7
  2.2 MCMC演算法	9
第三章 模擬結果	14
第四章 結論	28
參考文獻	29

圖目錄
圖3.1 混合韋伯分配曲線圖 β，θ_1 、θ_2= (a) (3.4，2.3，4.3) (b)  (3.4，2.3，6.3) (c) (3.4，4.3，6.3) (d) (5，2.3，4.3) (e) (5，2.3，6.3) (f) (5，4.3，6.3) (g) (10，2.3，4.3) (h) (10，2.3，6.3) (i) (10，4.3，6.3)。	16

表目錄
表3.1 c= 0.7之 β_1 、θ_1 、β_2 、θ_2 的偏差與均方誤差	19
表3.2 c= 0.7之 β_1 、θ_1 、β_2 、θ_(2 )的偏差與均方誤差	19
表3.3 c= 0.7之 β_1 、θ_1 、β_2 、θ_(2 )的偏差與均方誤差	20
表3.4 c= 0.7之 β_1 、θ_1 、β_2 、θ_(2 )的偏差與均方誤差	20
表3.5 c= 0.7之 β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	21
表3.6 c= 0.7之 β_1 、θ_1 、β_2 、θ_(2 )的偏差與均方誤差	21
表3.7 c= 0.7之 β_1 、θ_1 、β_2 、θ_(2 )的偏差與均方誤差	22
表3.8 c= 0.7之 β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	22
表3.9 c= 0.7之β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	23
表3.10 c= 0.5之 β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	23
表3.11 c= 0.5之β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	24
表3.12 c= 0.5之β_1 、θ_1 、β_2 、θ_(2 )的偏差與均方誤差	24
表3.13 c= 0.5之 β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	25
表3.14 c= 0.5之 β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	25
表3.15 c= 0.5之 β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	26
表3.16 c= 0.5之 β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	26
表3.17 c= 0.5之 β_1 、θ_1 、β_2 、θ_2  的偏差與均方誤差	27
表3.18 c= 0.5之 β_1 、θ_1 、β_2 、θ_(2 )的偏差與均方誤差	27

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