802.16與802.11e均有提供服務品質(QoS)，但是其MAC並不相同，為了達到QoS的保證，我們使用馬可夫鍊(Markov Chain)模型分析在不同連線數量時802.11e EDCA的延遲時間(delay time)。然後，我們可以再利用允入控制(CAC)機制限制連線的數量以保證延遲時間的需求，並使用令牌桶(Token Bucket)機制，在滿足延遲及頻寬的需求下控制輸出流量，在我們的令牌桶機制中可以依照頻寬需求的變化自動調整令牌(Token)產生速率，最後使用封包丟棄機制提升吞吐量(throughput)。 　　在提出我們的方法後，我們使用Qualnet模擬器驗證延遲時間、封包丟棄率及吞吐量，結果表示我們所提出的方法在三方面都有明顯的改進。 / IEEE 802.16 and 802.11e both provide Quality of Service (QoS), but the MAC of betweens is different. Ensuring the QoS guarantee, we use a Markov Chain model to analyze the 802.11e EDCA delay time under variance number of connections. Therefore, we can employ a CAC mechanism constraining the number of connections to guarantee the delay requirement. Further, considering the delay requirement and the bandwidth, we use a Token Bucket mechanism to throttle the traffic output that ensures the delay and bandwidth to be satisfied. And our Token Bucket mechanism can tune the token rate automatically by bandwidth requirement. Finally, we use the Packet Drop mechanism to improve throughput. After my methodology, we validate the delay, packet drop rate and throughput by simulator Qualnet. We have significant improvement in delay, drop rate, and throughput.

近年來,許多調適性管制圖都只探討單一製程,然而現今存在許多相依製程的問題.因此本論文提出兩相依製程之調適性管制圖,並以ATS測量管制圖的績效.本論文所提出的變動抽樣間隔時間之調適性管制圖對於偵測製程中幅度及小幅度的偏移有良好的績效.此外,本論文所提出的變動抽樣樣本大小及變動抽樣間隔時間之調適性管制圖對於偵測製程極小幅度的偏移有良好的績效. / In recent years, many research papers about adaptive control charts all consider a single process step. However, there are many multiple process steps in industry process. In this article, we propose adaptive control charts to monitor two dependent process steps, and their average time to signal (ATS) is calculated by Markov chain approach to measure the performance of these proposed control charts. It has been shown that the performance of the adaptive sampling interval (ASI) control charts in detecting small and moderate shifts in process means is better than the fixed sampling interval control charts, especially for small shifts, and the proposed adaptive sample size and sampling interval (ASSI) control charts have better performance in detecting very small shifts in process means than the fixed sample size and sampling interval control charts and the adaptive sample size control charts.

分量迴歸在近幾年來的應用相當廣泛，但透過貝氏方法估計分量迴歸參數，是由Yu & Moyeed（2001）所提出，拜電腦運算發達之賜而生的新估計方法，因此在實證應用上的研究，貝氏分量迴歸仍在起步的狀態。並且應用馬可夫鍊蒙地卡羅方法的貝氏分量迴歸，在後驗分配的收斂上並沒有類似的探討文獻。因此本研究嘗試以馬可夫鍊蒙地卡羅方法的應用觀點出發，研究運用貝氏方法的分量迴歸估計是否達到馬可夫鍊所重視的收斂至穩態分配，也就是利用模擬資料，探討使用馬可夫鍊蒙地卡羅方法的貝氏分量迴歸在何種情況下，具有較好的收斂情形，以及選擇適當的提議分配。接著以台灣上市公司為例，依電子、紡織以及塑膠產業為別，利用貝氏分量迴歸，觀察民國86~90年，以及91~95年兩區間，股價報酬率在各分量下與財務比率的關連性，並依產業分別進行探討。 本論文研究結果指出，貝氏分量迴歸在使用時仍須注意馬可夫鍊的收斂情形，將馬可夫鍊的接受頻率定在約20%~30%為佳，且估計結果與Koenker & Bassett（1978）所提出的無母數方法相當一致。在實證資料的分析上，以電子、紡織以及塑膠產業各別的配適結果來看，都依產業別的不同而具有合理的解釋，但貝氏分量迴歸容易因自變數值域的問題，造成馬可夫鍊接受頻率不理想，以及收斂速度過慢的情形，因此在應用貝氏分量迴歸時，自變數值域的影響需要納入考慮，並仍須選擇適當的提議分配、馬可夫鍊重複次數，所得到的結果才會較佳。

This paper follows Ghysels, Gourieroux, and Jasiak (1998), examines the causal relation between price and volume in Taiwan Futures Market. I use high frequency intraday data of Taiwan Stock Exchange Capitalization Weighted Stock Index in Taiwan Futures Exchange; and analyze the causality between returns and volume series, which are transformed into Markov chain, with Granger’s causal tests. I analyze the data with two different time category, trading time and calendar time. In our research we find out that Taiwan futures market has a bi-directional causality between price and volume in trading time analysis, as to the calendar time analysis, only price to volume unidirectional causality exists. Unlike the unidirectional causal relation that Ghysels, Gourieroux, and Jasiak (1998) observed in French security market.

本文模型來自於DeGroot (1974)的模型，以此模型為基礎延伸至兩產品之架構，並利用比較效果與網絡效果疲乏之間的交互作用，而得到了本文的定理2.。此定理2.可解釋許多市場上，廠商選擇其產品推出時間而影響其銷售量之變化。因此，本文對於兩廠商的競爭提出了一個新觀點：廠商因何時推出其產品，而能使得其產品成為主流。我們討論的不是價格或者品質的競爭，而在於產品相對推出的時間，因此，本文在定理2.得到了一個充分條件，這個條件讓廠商知道應何時推出其產品，而此產品可成為市場上之主流產品。

自2007年金融海嘯爆發以來，至目前2012年的歐債危機，銀行部門就不斷成為風暴的中心。雖然金融危機並非金融海嘯或歐債危機的起因，但卻像是一種經濟衰退放大的機制。如何針對銀行部門進行有效的監管，自上世紀末90年代就已經開始進行討論。然而，經過十年來的發展，仍然發生雷曼兄弟倒閉等事件的金融危機。除了銀行的表現之外，是否仍有其他因素會影響銀行的經營績效？因此，本文欲探討：銀行的控制股東，是否會影響銀行經營決策的方向，進而產生不同的營運表現。 本文先分析集團內部股份盈餘比差異，分析集團內部是否存在剝奪的動機。其中，相較於目前計算金字塔結構的現金流量權的方法，本文發展馬可夫鍊計算現金流量權(盈餘分配權)。除了金字塔結構之外，本方法亦可精確地計算交叉持股或者較複雜的股權結構的現金流量權。本文並利用簡單回歸，分析集團持股銀行的比例，是否會對銀行績效產生影響，藉此觀察其中產生的剝奪問題。本文進一步分析，當控制股東進入董事會之後，其董事會代表持股銀行比例，是否也會對銀行績效產生影響，產生剝奪問題。最後，本文歸納出可能發生的剝奪機制：控制股東利用放款的途徑，因而導致逾期放款比例的增加，降低銀行營運表現，進而達到剝奪的效果。

The CUSUM control charts have been widely used in detecting small process shifts since it was first introduced by Page (1954). And recent studies have shown that adaptive charts can improve the efficiency and performance of traditional Shewhart charts. To monitor the process mean and variance in a single chart, the loss function is used as a measure statistic in this article. The loss function can measure the process quality loss while the process mean and/or variance has shifted. This study combines the three features: adaption, CUSUM and the loss function, and proposes the optimal VSSI, VSI, and FP CUSUM Loss chart. The performance of the proposed charts is measured by using Average Time to Signal (ATS) and Average Number of Observations to Signal (ANOS). The ATS and ANOS calculations are based on Markov chain approach. The performance comparisons between the proposed charts and some existing charts, such as X-bar+S^2 charts and CUSUM X-bar+S^2 charts, are illustrated by numerical analyses and some examples. From the results of the numerical analyses, it shows that the optimal VSSI CUSUM Loss chart has better performance than the optimal VSI CUSUM Loss chart, optimal FP CUSUM Loss chart, CUSUM X-bar+S^2 charts and X-bar+S^2 charts. Furthermore, using a single chart to monitor a process is not only easier but more efficient than using two charts simultaneously. Hence, the adaptive CUSUM Loss charts are recommended in real process. / The CUSUM control charts have been widely used in detecting small process shifts since it was first introduced by Page (1954). And recent studies have shown that adaptive charts can improve the efficiency and performance of traditional Shewhart charts. To monitor the process mean and variance in a single chart, the loss function is used as a measure statistic in this article. The loss function can measure the process quality loss while the process mean and/or variance has shifted. This study combines the three features: adaption, CUSUM and the loss function, and proposes the optimal VSSI, VSI, and FP CUSUM Loss chart. The performance of the proposed charts is measured by using Average Time to Signal (ATS) and Average Number of Observations to Signal (ANOS). The ATS and ANOS calculations are based on Markov chain approach. The performance comparisons between the proposed charts and some existing charts, such as X-bar+S^2 charts and CUSUM X-bar+S^2 charts, are illustrated by numerical analyses and some examples. From the results of the numerical analyses, it shows that the optimal VSSI CUSUM Loss chart has better performance than the optimal VSI CUSUM Loss chart, optimal FP CUSUM Loss chart, CUSUM X-bar+S^2 charts and X-bar+S^2 charts. Furthermore, using a single chart to monitor a process is not only easier but more efficient than using two charts simultaneously. Hence, the adaptive CUSUM Loss charts are recommended in real process.