本研究主要針對 Breidt et al.(1991) 等多位學者所建構的 Mixed causal-noncausal model,探討其假設與可拆解特性,並仔細討論相關資料模擬估計及預測的方法,最後將其實際應用於隱含波動率指數 (Volatility Index)的估計及預測上。根據本研究的實證結果,我們發現隱含波動率指數確實包含非因果的特性,並可進一步對其拆解及預測。另外 , 我們也以移動窗格的方式觀察係數估計結果的變化,發現 Mixed Causal-Noncausal Model 的確能夠捕捉到泡沫或危機正在生成的過程。 / This paper first focuses on Mixed causal-noncausal model constructed by Breidt et al.(1991) and then conducts empirical research on the CBOE Volatility Index. The assumptions, simulation, estimation and prediction methods of Mixed causal-noncausal model are introduced in great detail. Our empirical results show that the CBOE Volatility Index really contains non-causal parts, such that we can filter this part from the index and then further predict it. Moreover, by employing the rolling window estimation scheme the resulting coefficients of Mixed causal-noncausal model really could detect a bubble or a crisis which is going to happen.

本研究主要目的是想要探討台灣地區民眾對於維持公共電視的營運與發展之願付價格。資料來自於中央研究院人文社會科學研究中心調查研究專題中心所進行的一項公共電視願付價格調查，其中關於願付價格的部分是透過條件評估法的方式取得，受訪者隨機分配至兩個題組之一，其中A題組為考慮公共電視帶給家人的好處，而B題組為考慮公共電視帶給全國民眾及社會的好處。 建模時我們採用一或二要素混成模型，這是一個可以將不理性受訪者分離，僅針對理性受訪者的願付價格進行估計的一個模型。分析結果顯示考慮到公共電視帶給家人的好處時，年齡50歲以上的受訪者中存在著一群無論什麼價格都不願意支付或Ney-sayers的人，估計的比例大約為21.13%；而考慮到公共電視帶給全國民眾及社會的好處時，很少看文化教育節目的受訪者中也存在著一群無論什麼價格都不願意支付或Ney-sayers的人，估計比例大約為13.53%。 針對願意支付合理價格的受訪者配適加速失敗模型時，我們不僅就位置參數引進解釋變數，同時也引進解釋變數至尺度參數。分析結果顯示願意支付合理價格的受訪者，在考慮公共電視帶給家人的好處時，每年願意支付價格之平均值為1477元；而考慮到公共電視帶給全國民眾及社會的好處時，每年願意支付價格之平均值為1663元。顯示受訪者在考慮到全國民眾及社會的好處，願意付出較高的價錢。 / This study aimed to explore people’s willingness to pay (WTP) for maintaining the operation of Public Television Service (PTS) in Taiwan. The survey using contingent valuation method was conducted by the Center for Survey Research, Academia Sinica. The survey sample was split into two groups, A and B. Each group was presented with the same scenario but different scope of benefits. Group A considered the benefits that PTS might bring to one’s family, while Group B considered the benefits that PTS might bring to the whole society. The model used in this study was a one/two-component hybrid model, a model that is able to separate those who are willing to pay a reasonable price from these who are not, and obtain their mean WTP estimate. Multinomial logistic part of the model, indicated that for Group A, among those who were 50 years of age or older, about 24.48% were not willing to pay any price or Ney-sayers. And for Group B, among those who seldom watched cultural or educational programs, about 12.91% were not willing to pay any price or Ney-sayers. Appling accelerated failure time model to those who were willing to pay for reasonable prices enabled us to evaluate the WTP. We not only introduced explanatory variables in the location parameter but also the scale parameter. The estimated mean WTP for Group A was found to be NT$1477 per year, while the mean WTP for Group B was NT$1663 per year.

隨著資訊科技的發展與網際網路的普及，音樂與電影光碟盜版的問題也逐年嚴重，然影音盜版不僅影響權利人團體，影音業者及創作者之生存，亦攸關我國智慧財產之發展，更常成為我國在國際貿易諮商上的重要課題。在種種緣由與現況下，得使國內許多產、官、學、研團體想去研究影音盜版的相關議題，以了解其嚴重程度如何，或有無較客觀合理的指標或評估方式?並進一步研擬有效方法來防範盜版問題進一步惡化，為此上述問題乃是本研究之源起。 目前所有的影音盜版研究，多著重在計算盜版率,探討盜版因素,盜版行為的心理與法制問題，皆還尚未針對影音盜版率，建構出可供學者推論的盜版率機率分配，及其他相關的數量研究，因此，本研究的主要實証方向，乃根據2004年經濟部智慧財產局(intellectual property office ministry of economic affairs,R.O.C)委託政治大學之消費者調查資料，就音樂CD、影音VCD/DVD兩部分，針對筆者有興趣之變項(性別、年齡、有無上網下載等)，(1)分別建構各自的混合分配並了解其分配間的差異與趨勢， (2)探討消費者對盜版行為的態度，(3)了解消費者對喜好的光碟所願付價格之差異，(4)建立盜版率分配的信賴帶，以及(5)針對現有的調查資料進行盜版辨別。 最後，就查緝盜版與維護智慧財產權兩方面，實證分析提供政府相關單位作為參考的依據，以求擬訂周詳且完善的措施來防範日益惡化的盜版問題。 / With the development of computer technology and widespread of internet, the piracy problem goes more serious. The piracy situation makes much influence not only on the rights of international oblige societies but also the growing of the intellectual properties in Taiwan. Moreover, it becomes the rock on the road of international commercial negotiations. Beyond the serious situation in the mean time, more researchers and relevant organizations on the island are trying to pay more attention to this important issue. This research intends to understand several questions: How is the actual situation on the piracy problem? Are there any objective evaluation ways? Are there any effective policies to prevent it from going deeper? These questions lead to this research. In the meantime, most of Audio & Video piracy research emphasized only on calculating the piracy rate, or the reasons, or the relevant psychological and law problems, but few on piracy quantitative studies. Therefore the mainly intention of this research is based on the data from the IPO(Intellectual Property Office Ministry of Economic Affairs, ROC), which is executing by National Chengchi University. As for the two parts concerning music CD and visual VCD/DVD, and the variables those I am highly interested including gender, age, education level, downloading or not. The empirical study results show as below: (1)The piracy rate distribution corresponds with the Mixed Model, that mean that it have been proportionally mixed two degenerate distribution (while X=0 and 100) with the Normal distribution. (2) On the facets of distribution differences and trends analysis, not only music CD and visual VCD/DVD, the results of the research by Mann-Whitney test and Kolmogorov-Smirnov two sample test both reveal the rising tendency of overall piracy rate. The generation of 20~29 years old is the mainly pirate group, moreover, higher education grades group does the more pirating behaviors, and lower income group intends more unauthorized copying conducts. Furthermore, along with the development of internet technology, the infringement behavior is more serious on the network connectors than the non-network downloaders. (3) Under surveying the opinions of consumers about the piracy, regardless of whether music or movies, the deviation is more serious on male than female, under 30-year-old than above, low educated than high, low income than high, pirate than non-pirate, downloaders than non-downloaders. The problem locates not only the lack of the concepts and recognition on the intellectual properties rights, but also the scarce of moral or legal limitations on the unauthorized rebuilding or downloading. But in the other curious facet, although the higher grade educated groups got more equitable standpoints on the piracy discussion, but evidenced depend upon the collected data they are also mainly the group who did the piracy behaviors more. (4) On the price range that a consumer would like to pay for, most of the pirate consumer tends to pay low price to buy the A/V goods, most of the non-pirating consumer group tends to pay general price to buy ones, and no significant difference of these two groups with high price, (5) On the facets of confidence bands on the whole music CD and visual VCD/DVD pirating rate, because of the specialties of pirating data- the higher frequency while the piracy rate values 0 and 100, so that the upper and lower bound reveals at 0 and 100. Futhermore, the confidence bands obtains from the population distribution function, therefore it’s suitable for the goodness-of-fit test. The results met the Kolmogorov-Smirnov one sample test. (6) On the data recognition facets, the logistic regression model of piracy is constructed in this research. Classification from the fitted logistic regression models, the results reveals 107 non-pirate are mis-judged to pirating behaviors, 186 pirating samples are neglected to non-pirate ones, the correct recognition rate goes high of 88 %. Key Words：Piracy Rate, Mixture Models, Mann-Whitney, Kolmogorov-Smirnov, Logistic Regression Model, Nonparametric Statistics.

互聯網的發展與近年來數據平台受到公私部門重視，資訊的取得與流通變得便捷，中國房地產文化目前有別於台灣，尚無實價登錄機制且地域面積廣大，傳統估價模型可能無法直接應用，面對房地產背後眾多的影響因素，本研究將預測建模目標放在泡沫化尚不嚴重且較具有潛力的中國新一線城市杭州市，自新浪二手房網爬取杭州市房地產數據，並自國家統計局取得各地區行政支出數據，作為實證分析資料。結合自動程序爬蟲抓取數據、統計分析與機器學習方法，期望對中國房地產建立一混合非監督式與監督式學習之模型。 在分群結果之後建構模型採用之技術為C5.0、三層CHAID、五層CHAID與Neural Network，挑選出最適合的模型為使用混合模型後的C5.0決策樹方法，達到降低變數維度亦提升或達到相當的預測準確率的雙贏目標，模型中行政地區、面積、總樓層為最頻出現的重要變數。 另外透過集群分析於行政支出的應用，發現2016年度杭州市投入的行政支出集中於余杭區、蕭山區、濱江區，成為賣屋及購屋者的第二項決策標準。 / In recent years, with the growth of the Internet and the importance of data platform on public sector and private sector. Getting and sharing information are made easily. The culture of real estate in China is different from Taiwan. For instance, there is no actual house price registration system. Furthermore, traditional estimate model may not be directly applicable to China which has the vast geographical area of the mainland. There are many factors to influence house price model. This study focus on Hangzhou city. Because the burst of real estate bubbles is not serious as first-tier cities and it is one of new first-tier cities in China. The research data were crawler from Sina second-hand housing website and National Bureau of Statistics. By using auto web crawler skill, statistical analysis, and machine learning method to build a real estate model in China, which was combining unsupervised learning method with supervised learning method. After clustering Hangzhou second-hand housing data, this study used C5.0, three layers Chi-Square Automatic Interaction Detector(CHAID), five layers CHAID, and Neural Network(NN). The study goal are both reducing dimension and getting better forecast accuracy. Choosing clustering- C5.0 model as appropriate house price model to achieve win-win situation after comparing final result. Administrative region, area, and total floor are the top three high frequency influential factors. Applying Clustering Analysis to administrative expenses data in Hangzhou, the study found that the government resource focus on Yuhang, Xiaoshan, and Binjiang. It can be the second decision-making criterion for house sellers and house buyers.

為了處理在願付價格的研究中，極端受訪者對於估計結果所造成的誤差。本文沿用Tsai(2005)所建議採用的三要素混合模型，將受訪者區分為價格再高都願意支付、願意支付合理價格以及價格再低都不願意支付等三種類型。在評估願付價格時，以加速失敗模型(accelerated failure time model，簡稱AFT model)針對願意支付合理價格的受訪者進行估計，並且在考慮不同起價點可能會造成不同程度的起價點偏誤(starting point bias)或是定錨效果(anchoring effect)的情形下，提出一個起價點偏誤調整模型來做探討。我們並以CVDFACTS中的高血壓之願付價格資料進行實證分析。分析結果發現，教育程度越高的男性對於能降低高血壓病患罹患心臟血管相關疾病之新藥願意付較高的金額。此外我們也發現在此筆資料中，不同起價點確實會造成不同程度的偏誤，經由偏誤調整後會得到較高的願付金額。 / A study of willingness-to-pay often suffers from the bias introduced by extreme respondents who are willing to or not willing to pay any price. To overcome the problem, a three-component model proposed by Tsai (2005) is adopted. Under such a circumstance, respondents are classified into three categories, i.e. respondents who are willing to pay any price, unwilling to pay any price, or willing to pay a reasonable price. The willingness-to-pay for those subjects who are willing to pay a reasonable price is again modeled by an accelerated failure time model (AFT model). In this study, we, however, propose an unified model that allows us to look into the issue related to starting point bias and anchoring effect, simultaneously. Willingness-to-pay for cardiovascular disease treatment from a longitudinal follow-up survey- CVDFACTS, is investigated using the new model. Through the use of the model, we are able to detect the effects of starting point biases, and make a proper adjustment accordingly. Our analysis indicates that male respondents with higher education level have an inclination to pay higher price for the new treatment. Besides, we also discover that starting point bias does exist in this dataset.

股價的漲跌變化是由於證券市場中眾多不同投資人及其投資決策後所產生的結果。然而，影響股價變動的因素眾多且複雜，新聞也屬於其中一種，新聞事件不但是投資人用來得知該股票上市公司的相關營運資訊的主要媒介，同時也是影響投資人決定或變更其股票投資策略的主要因素之一。本研究提出以新聞文件做為股價漲跌預測系統的基礎架構，透過文字探勘技術及分類技術來建置出能預測當日個股收盤股價漲跌趨勢之系統。 本研究共提出三種分類模型，分別是簡易貝氏模型、k最近鄰居模型以及混合模型，並設計了三組實驗，分別是分類器效能的比較、新聞樣本資料深度的比較、以及新聞樣本資料廣度的比較來檢驗系統的預測效能。實驗結果顯示，本研究所提出的分類模型可以有效改善相關研究中整體正確率高但各個類別的預測效能卻差異甚大的情況。而對於影響投資人獲利與否的關鍵類別"漲"及類別"跌"的平均預測效能上，本研究所提出的這三種分類模型亦同時具有良好的成效，可以做為投資人進行投資決策時的有效參考依據。 / Stocks' closing price levels can provide hints about investors' aggregate demands and aggregate supplies in the stock trading markets. If the level of a stock's closing price is higher than its previous closing price, it indicates that the aggregate demand is stronger than the aggregate supply in this trading day. Otherwise, the aggregate demand is weaker than the aggregate supply. It would be profitable if we can predict the individual stock's closing price level. For example, in case that one stock's current price is lower than its previous closing price. We can do the proper strategies(buy or sell) to gain profit if we can predict the stock's closing price level correctly in advance. In this thesis, we propose and evaluate three models for predicting individual stock's closing price in the Taiwan stock market. These models include a naïve Bayes model, a k-nearest neighbors model, and a hybrid model. Experimental results show the proposed methods perform better than the NewsCATS system for the "UP" and "DOWN" categories.

本文主要著重在處理條件評估法下，「不知道」受訪者的回應。當「不知道」受訪者的產生機制並未符合完全隨機時，考量他們的真實意向就顯得極為重要。 文中使用中央研究院生醫所在其研究計畫「竹東及朴子地區心臟血管疾病長期追蹤研究」（CardioVascular Disease risk FACtor Two-township Study，簡稱CVDFACTS）第五循環中的研究調查資料。 　　由於以往的文獻對於「不知道」受訪者的處理，皆有不足之處。如Wang (1997)所提出的方法，就只能針對某種特定的「不知道」受訪者來做處理；而Caudill and Groothuis (2005)所提的方法，由於將「不知道」受訪者的差補與願付價格的估計分開，亦使其估計結果不具備一些好的性質。在本文中，我們提出一個能同時處理「不知道」受訪者且估計願付價格的方法。除了使得統計上較有效率外，也保有EM演算法的一個特性：願付價格模型中的估計參數為最大概似估計值。此外，在加入三要素混合模型(Tsai (2005))後，我們也可避免用到極端受訪者的訊息去差補那些「不知道」受訪者的意向。 　　在分析願付價格的過程中，我們發現此筆資料的「不知道」受訪者，其產生的機制為隨機，而非為完全隨機，這意謂著不考量「不知道」受訪者的分析結果，必定會產生偏差。而在比較有考量「不知道」受訪者與沒有的情況後，其結果確實應證了我們的想法：只要「不知道」受訪者不是完全隨機產生的，那麼不考量他們必定會產生某種程度的偏差。 / This paper investigates how to deal with “Don’t Know” (DK) responses in contingent valuation surveys, which must be taken into consideration when they are not completely at random. The data we use is collected from the fifth cycle of the Cardiovascular Disease Risk Factor Two-township Study (CVDFACTS), which is a series of long-term surveys conducted by the Institute of Biomedical Sciences, Academia Sinica. Previous methods used in dealing with DK responses have not been satisfactory because they only focus on some types of DK respondents (Wang (1997)), or separate the imputation of DK responses from the WTP estimation (Caudill and Groothuis (2005)). However, in this paper, we introduce an integrated method to cope with the incomplete data caused by DK responses. Besides being more efficient, the single-step method guarantees maximum likelihood estimates of the WTP model to be obtained due to the good property that the EM algorithm possesses. Furthermore, by adding the concept of the three-component mixture model (Tsai (2005)), some extreme information are drawn out when imputing the DK inclinations. In this hypertension data, the mechanism of the DK responses is “Don’t know at random”, which means the analysis of DK-dropped results in a bias. By using our method, the difference between DK-dropped and DK-included is actually revealed, which proves our suspicion that a DK-dropped analysis is accompanied by a biased result when DK is not completely at random.