在有限的資源之下，各區域地方政府相當渴望跳脫僵化與官僚的決策模式，尋求創新有效率的治理機制，另一方面開放政府資料已為國際化的趨勢，台灣於開放資料領域耕耘成果亦相當豐碩。本研究希望建置商業智慧平台，將開放資料轉換為無形的「智慧資本」，持續驅動創新有效率的「治理機制」，進而改善在地人民的生活品質。 本論文研究實作一網頁為基礎的商業智慧分析平台，工具包括資料包絡分析法、競爭者分析，透過探索式資料分析，使用者彈性操作指標與決策參數，反覆進行資料探索分析，進而了解（一）地方之競爭縣市與區域特色（二）各縣市相對治理績效（三）單一縣市之優勢產業。並藉由宜蘭縣的文創、觀光、環境此三個產業面向的資料為例說明。 本論文聚焦於使用前端框架技術—AngularJS之系統實作，藉由資料視覺化設計、提升使用者經驗，建置高擴充性的資料探勘分析的平台，更可滿足使用者一次購足的統計資料查詢環境。 / Facing the challenges of limited resources and budget constraints, regional governments have been actively pursuing strategies to transform conventional bureaucratic decision-making model into innovative and efficient governance mechanism. At the same time, “open government data” is becoming a political commitment for many countries and Taiwanese government has made significant advances in this respect recently. To leverage the trend for open public data, this thesis aims to develop a web-based business intelligence system to support efficient governance through in-depth analysis of intellectual capital. The tools provided in this system include data envelopment analysis (DEA), competitor identification, and exploratory data analysis. The system is designed to allow average users to experiment with different parameter settings and view the results interactively. Insights on competing counties and regional characteristics, relative governance efficiency and leading industry can be gained with ease. We illustrate the functionalities of the system using data from Yi-Lan County and investigate its competitiveness in three areas, namely, culture and creative industry, tourism, and environmental industry. AngularJS, a front-end framework, is utilized to implement the proposed business intelligence system. The objective is to provide a one stop shopping service for interactive data analysis and visualization with user friendly design and good extensibility.

函數型資料分析為近年發展的統計方法。函數型資料是在一段特定時間上，我們只在離散的時間點上收集觀測值。例如:氣象觀測站所收集到的每月氣溫、雨量資料，即是一種常見的函數型資料。函數型資料主要有三種特色，共同趨勢性、觀測個體反應強度不同，觀測個體時間特色上的差異。本文研究主要是使用，Brumback與Lindstrom在2004所提出的自模型迴歸族(self-modeling)當作模型架構來處理函數型資料的趨勢性與個體反應強度。而為了處理函數型資料的時間差異性，我們在模型中加入時間轉換函數(time transformation function)，處理函數型資料的時間差異性步驟，這個過程稱為同步化。經過同步化的處理後，能幫助研究者更清楚資料的特性。模型中除了時間轉換函數的部份，其餘模型中的參數我們是利用馬可夫鏈蒙地卡羅法中的Gibbs Sampling來進行參數的抽樣，並以取出的抽樣值來估計參數。時間轉換函數的部份，我們使用概似懲罰函數(penalized likelihood function)來估計時間轉換函數的參數部份。由於函數型資料擁有趨勢性，我們預期不同類別的資料，會呈現不同的趨勢性，我們將利用此一特色當做分類上的標準。 關鍵詞:函數型資料分析、曲線同步化、曲線區別分析、馬可夫鏈蒙地卡羅法。 / Functional data are random curves observed in a period of time at discrete time points.They often exhibit a common shape, but with variations in amplitude and phase across curves.To estimate the common shape,some adjustment for synchronization is often made,which is also known as time warping or curve registration.In this thesis,splines are used to model the warping functions and the common shape. Certain parameters are allowed to be random.For the estimation of the random parameters,priors are proposed so that samples from the posteriors can be obtained using Markov chain Monte Carlo methods.For the estimation of non-random parameters, a penalized likelihood approach is used. It is found via simulation studies that for a set of random curves with a common shape,the estimated common shape function looks like the true function up to a location-scale transform,and the curve alignment based on estimated time warping functions looks reasonable.For two groups of random curves which differ in the group common shape functions,synchronization also improves the discrimination between groups in some cases. Key words: functional data analysis,curve registration,curve discrimination,markov chain monte carlo method.

隨著資訊技術的進步，各種史料文本的數位化工作已經處理完成，運用資訊技術於史料文本分析的研究日益增加。本研究以詞頻分析與社會網路分析為主軸，對於古代《易經》爻辭的文字進行多元化的觀察，本研究首先以詞頻分析探討《易經》爻辭字詞頻率的觀察，再利用《易經》爻辭位置資訊建構成各個社會網路結構，對每個社會網路結構運算各項社會網路指標數據，最後將實驗結果與過往《易經》爻辭的論點做印證與對照，期望對於《易經》爻辭之分析，有更多元性的客觀研究觀察。本研究提供了一個分析《易經》爻辭的新面向，也可供未來研究者對於其他古文研究作參考。 / With advances in information technology, digitization of various historical text has been completed.The study of historical text analysis by using information technology is in-creasing daily.In this paper, we used word frequency analysis and social network analy-sis in the I-Ching line statements.First, we used word frequency analysis in I-Ching line statements,using N-gram and TF-IDF technique analysis word frequency.Second, we constructed social network structure by I-Ching line statements position infor-mation,calculating several social network analysis indicator on each network.We com-pared our experiment results with some existing I-Ching theory, expecting to get more objective results and more diverse analysis for the I-Ching line statements. We not only provided a new perspective to study I-Ching line statements but also expected to help other researchers to study different historical text.

本研究以1993年到2017年3月間的193篇FOMC Minutes作為研究素材，先採監督式學習方法，利用潛在語意分析（latent semantic analysis，LSA）萃取出升息、降息及不變樣本的潛在語意，再以線性判別分析(Linear Discriminant Analysis, LDA)進行分類；此外，本研究亦透過非監督式學習方法中的探索性資料分析(Exploratory Data Analysis, EDA)，試圖從FOMC Minutes中找尋相關變數。研究結果發現，LSA可大致區分出升息、降息及不變樣本的特徵，而EDA能找出不同時期或不同類別下的重要單詞，呈現文本的結構變化，亦能進行文本分群。 / In this study, 193 FOMC Minutes from 1993 to March 2017 were used as research materials. The latent semantic analysis (LSA) in supervised learning methods was used to extract the potential semantics of interest rate increased, decreased, and unchanged samples, and then linear discriminant analysis (LDA) was used for classification. In addition, this study attempts to find relevant variables from FOMC Minutes through exploratory data analysis (EDA) in unsupervised learning methods. The results show that LSA can distinguish the characteristics of interest rate increased, decreased, and unchanged samples. EDA can find relevant words in different periods or different categories, show changes in the text structure, and can also classify the texts.

【目的與理論】 探討「統治現象」的方法有二：一是透過制度研究的方式，第二則是進行統治菁英的研究。本文即是以「人」的角度出發，探討國民黨、民進黨政府執政菁英的生態與差異。並以代表理論延伸至「代表性中央執政菁英」的觀點，以兩千年台灣社會人口統計作為檢視這兩個政權的執政團隊是否具有社會代表性的標準。預期透過這樣的研究，去理解一個統治台灣長達五十年的國民黨，以及成立於一九八六年的新興民進黨，這兩個背景迥然不同的政黨，執政團隊的結構究竟有什麼樣的差異，而其趨勢為何…等，進行初始性的探索與研究。 【研究方法】 本文為國內首起有關中央政府執政菁英組成之比較性研究，必須透過蒐集國史館網站、行政院暨所屬各機關人事名錄、中華民國名人錄…等次級資料，來進行本研究資料庫的建立。預期透過比較兩政府執政菁英的黨籍、性別、省籍、區域、世代及前職等個人變項，去理解這兩個政權是否因為政黨間特色的差異，所以形成了不同的執政菁英生態，並以代表理論作為呼應。 【研究結論】 分析新、舊政權執政菁英的組成生態後，發現落實兩性共治理念的限制、世代的交替、外省族群勢力的全面性撤退、雲嘉南政治菁英的崛起及民進黨政府取才管道的多樣化等。透過比較由遠至近的時間點，印證了執政菁英的組成生態（例如性別、世代、族群及區域等變項）在符合台灣社會人口的代表性上，不是政權交替之後才急遽地產生變化，而是逐漸改變的，雖然其在改變的幅度上有所差異。再者，民進黨政府於取得執政權後逐漸於政務層級中置入實際的政治權力，全民政府的理念僅是首次組閣的政治性宣傳。最後，民進黨執政團隊於可替代性較高的部門及政務層級中，社會人口的代表性較易達成。 / This thesis is an exploratory study of the composition of governing elites on Taiwan. Secondary data analysis is the primary method used in this research. The data of elite profiles are mainly collected from the following sources: (1) a website database on historical figures from the Academia Historica; (2) the official address-book on superintendents of the Executive Yuan and its departments; (3) Who's Who of the Republic of China. This research compared personal variables of governing elites of two regimes, such as party membership, gender, ethnic group, area, generation, and pervious profession. In this way, this study tries to answer whether the characteristic differences between KMT and DPP governments will lead to the different characteristics of the governing elites as depicts in the theory of representative bureaucracy. After analyzing the composition of governing elites of the two regimes, we have following five findings. First, there is a long way to go to gender equality in governing elites regardless a little progress has been made by the DPP. Second, governing elites of DPP government are younger than KMT government. Third, much less elites of mainland Chinese origin are appointed by the DPP than the KMT. Forth, DPP appointed much more southern Taiwanese elites than the KMT does. Lastly, governing elites in DPP government are coming from a more diverse prior professions than those in the KMT government.

隨著經濟繁榮和網絡發展的日新月異，線上線下每時每刻都產生龐大數據，其中約有80%的文字、影像等非結構化數據，如何量化和採取適合的分析方法，成為有效提取有價值信息及對其加以利用的關鍵。針對文字類型的資料，本文提出探索性資料分析方法，並以《新青年》雜誌的語言變化為例，呈現如何選取文本特徵并对其量化及分析的過程。 首先，本文以卷為分析單位，多角度量化《新青年》雜誌各卷的文本結構，包括文本用字、用句、文言和白虛字使用以及常用字詞共用等方面，通過多種圖表相結合的呈現方式，窺探《新青年》雜誌語言變化歷程以及轉變特點。這其中既包括了對文言文到白話文轉變機制的探索，也包括白話語言演化的探索。其次，根據各卷初探的結果，尋找可區隔文言文和白話文兩種語言形式的文本特徵變數，再以《新青年》第一卷和第七卷為訓練樣本，結合主成分和羅吉斯迴歸，對文、白兩種語言形式的文章進行分類訓練，再利用第四卷進行測試。結果證實，所提取的文本變數能夠有效實現對文、白兩種語言形式的文章的區分。此外，本文亦根據前述初探結果以及人文學者經驗，探索《新青年》雜誌後期語言形式的變化，即從五四運動時期的白話文至以「紅色中文」為特徵的白話文（二戰之後中國使用的白話文）的變化。以第七卷和第十一卷為樣本進行訓練，結果證實這兩卷語言形式存在明顯區別；並加入台灣《聯合報》和中國大陸的《人民日報》進行分類預測，發現兩類報刊的語言偏向有明顯差異，值得後續深入研究。 / Tremendous data are produced every day, due to the rapid development of computer technology and economics. Unstructured data, such as text, pictures, videos, etc., account for nearly 80 percent of all data created. Choosing appropriate methods for quantifying and analyzing this kind of data would determine whether or not we can extract useful information. For that, we propose a standard operating process of exploratory data analysis (EDA) and use a case study of language changes in New Youth Magazine as a demonstration. First, we quantify the texts of New Youth magazine from different perspectives, including the uses of words, sentences, function words, and share of common vocabulary. We aim to detect the evolution of modern language itself as well as changes from traditional Chinese to modern Chinese. Then, according to the results of exploratory data analysis, we treat the first and seventh volumes of New Youth magazine for training data to develop classification model and apply the model to fourth volume (i.e., testing data). The results show that the traditional Chinese and modern Chinese can be successfully classified. Next, we intend to verify the changes from modern Chinese of the May 4th Movement to those by advocating Socialism. We treat the seventh volume and eleventh volume of New Youth magazine as training data and again develop a classification model. Then we apply this model to the United Daily News from Taiwan and People’s Daily from Mainland China. We found these two newspapers are very different and the style of United Daily News is closer to that of seventh volume, while the style of People’s Daily is more like that of eleventh volume. This indicates that the People’s Daily is likely to be influenced by the Soviet Union.

資本資產定價模型 (CAPM) 由Sharp (1964)、Lintner (1965)及Black (1972)發展出後，近年來已被廣泛的應用於衡量證券之預期報酬率與風險間之關係。一般而言，衡量結果之估計有兩個階段，首先由時間序列分析估計出貝它(beta)係數，然後再檢定廠商或投資組合之平均報酬率與貝它係數之關係。 Fama與MacBeth (1973)利用最小平方法估計貝它係數，再將由橫斷面迴歸方法所得出之斜率係數加以平均後，以統計t-test檢定之。然而以最小平方法估計係數，其估計值很容易受離群值之影響，因此本研究考慮以穩健估計 (robust estimator)來避免此一問題。另外，本研究亦將長期資料分析 (longitudinal data analysis) 引入CAPM裡，期望能檢定貝它係數是否能確實有效地衡量出系統性風險。 論文中以台灣股票市場電子業之實證分析來比較上述不同方法對CAPM的結果，資料蒐集期間為1998年9月至2001年12月之月資料。研究結果顯示出，穩健估計相對於最小平方法就CAPM有較佳的解釋力。而長期資料分析模型更用來衡量債券之超額報酬部分，是否會依上、中、下游或公司之不同而不同。 / The Capital Asset Pricing Model (CAPM) of Sharp (1964), Lintner (1965) and Black (1972) has been widely used in measuring the relationship between the expected return on a security and its risk in the recent years. It consists of two stages to estimate the relationship between risk and expected return. The first one is that betas are estimated from time series regressions, and the second is that the relationship between mean returns and betas is tested across firms or portfolios. Fama and MacBeth (1973) first used ordinary least squares (OLS) to estimate beta and took time series averages of the slope coefficients from monthly cross-sectional regressions in such studies. However it is well known that OLS is sensitive to outliers. Therefore, robust estimators are employed to avoid the problems. Furthermore, the longitudinal data analysis is applied to examine whether betas over time and securities are the valid measure of risk in the CAPM. An empirical study is carried out to present the different approaches. We use the data about the Information and Electronic industry in Taiwan stock market during the period from September 1998 to December 2001. For the time series regression analysis, the robust methods lead to more explanatory power than the OLS results. The linear mixed-effect model is used to examine the effects of different streams and companies for the security excess returns in these data.

對於零碎且結構複雜的資料來源時，社會網路分析能夠給予整體性的觀察，還能檢視個體之間的關係。目前社會網路分析研究中，因為將網路退化至簡單連結關係，所以會遺失許多珍貴的資訊。而網路規模和型態隨著研究議題的不同，也會跟著增大或趨於多變，但動態網路分析能夠提供我們檢視每個時期，網路的變化或社群的形成或消失，甚至能知道節點間的互動影響。本論文研究，以政府人事異動資料為主，並且加入了其他政府組織的相關資料，建構出政府組織的從屬網路，並在每個網路快照中，擷取出重要的官員異動；每一筆人事異動都是一個事件的發生，而特任或簡任官員在本研究中視為重要事件，從這些重要事件的發生，我們能夠對每個時間的官員，使用EventRank的演算法做排名計算。最後能從時間的變化中，觀察出每個時期的佔有重要影響力的官員。 / To fragmented and complex structure data, social network analysis (SNA) can give an overall observation, but also view the relationship between individuals. Recent research in SNA is the degradation of the network link to a simple relationship but it will lose a lot of valuable information. The size and type of network with different research topics will follow the increase or rapidly changing, dynamic network analysis can provide our view of changes in the network or community to form or disappear in every period, even know the impact of the interaction between nodes. This thesis is based on the government official changes and other related data to construct manager-subordinate network of the government organization and capture the important interactions between officials in every network snapshot. An official change is the occurrence of an event and special level official changes in this study as a critical event. From these critical events, we can use the Event Rank algorithm to rank the officials. Finally, we can observe which official has more influence from the time changes.