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本研究用金融傳導的角度，從散裝海運產業切入，利用標普高盛商品指數、加權遠期運費協議指數、波羅的海運費指數、道瓊全球航運指數以及美元指數，以傳遞熵與BEKK-GARCH模型，探討2008年3月至2016年3月之散裝海運產業金融傳導因子，在多次金融危機中，散裝海運產業金融傳導因子的領先落後關係、短期報酬外溢效果與長期波動傳遞效果，以及對航運類股之影響。 本研究成果可從投資策略與經濟意涵兩方面呈現，在投資策略上，根據實證結果，在金融危機期間，資訊從道瓊全球航運指數流向波羅的海乾散貨運價指數，再流向加權遠期運費協議指數，代表股票市場領先運費市場，而運費市場又領先遠期運費協議市場，而每個期間的加權遠期運費協議指數對波羅的海乾散貨運價指數皆為正向顯著關係，波羅的海乾散貨運價指數與道瓊全球航運指數間皆為雙邊正向顯著關係，本研究建議預測波羅的海乾散貨運價指數的散裝海運產業業者與投資人，可以道瓊全球航運指數與加權遠期運費協議指數作為先行指標。 在經濟意涵方面，根據實證結果，金融危機期間，金融市場動盪程度提高，連帶影響散裝海運運價價格波動劇烈，使得散裝海運產業業者與投資人的避險需求提升，由於波羅的海乾散貨運價指數為散裝海運產業業者的每日報價，並非金融市場交易之結果，故散裝海運產業業者與投資人可以參考商品市場、股票市場、外匯市場及運費市場的資訊進行避險操作。

在資產組合的優化過程中，總是希望賺取穩定的報酬以及規避不必要的風險，也因此，風險的衡量在資產組合理論中至關重要，而A. Ahmadi-Javid(2011)發表證明以相對熵為基礎的熵風險值(Entropic Value-at-Risk，簡稱EVaR)是為被廣泛使用的條件風險值(Conditional Value-at-Risk，簡稱CVaR)之上界，且EVaR在使用上更為效率，具有相當優越的性質，而本文將利用熵風險值的約當測度，去修改傳統均值–變異模型，並以臺灣股市為例，利用基因模擬退火混合演算法來驗證其在動態架構下的性質及績效，結果顯示比起傳統模型更為貼近效率前緣。

札米亞京的文學作品中常帶有大量象徵、隱喻與反諷，使讀者在閱讀上不易理解。特別是非俄語母語者，由於缺乏對俄語文化概念的理解，常常對於文字中的特殊寓意難以領會。因此本論文就文化概念上的意義，與作家本人的哲學思考和創作思想，來分析與解讀札米亞京文學作品中的象徵意義。論文的主要內容以作家的四部小說《我們》、《洞窟》、《人類獵人》、《島民》為研究範圍，並以「火」和「水」的形像為本文研究對象。

由於電腦科技的快速發展，網際網路(World Wide Web；簡稱WWW)使得資料共享及搜尋更為便利，其中的網路搜尋引擎(Search Engine)更是尋找資料的利器，最知名的「Google」公司就是藉由搜尋引擎而發跡。網頁搜尋多半依賴各網頁的特徵，像是熵(Entropy)即是最為常用的特徵指標，藉由使用者選取「關鍵字詞」，找出與使用者最相似的網頁，換言之，找出相似指標函數最高的網頁。藉由相似指標函數分類也常見於生物學及生態學，但多半會計算兩個社群間的相似性，再判定兩個社群是否相似，與搜尋引擎只計算單一社群的想法不同。 本文的目標在於研究若資料服從多項分配，特別是似幾何分配的多項分配（許多生態社群都滿足這個假設），單一社群的指標、兩個社群間的相似指標，何者會有較佳的分類正確性。本文考慮的指標包括單一社群的熵及Simpson指標、兩社群間的熵及相似指標(Yue and Clayton, 2005)、支持向量機(Support Vector Machine)、邏輯斯迴歸等方法，透過電腦模擬及交叉驗證(cross-validation)比較方法的優劣。本文發現單一社群熵指標之表現，在本文的模擬研究有不錯的分類結果，甚至普遍優於支持向量機，但單一社群熵指標分類法的結果並不穩定，為該分類方法之主要缺點。 / Since computer science had changed rapidly, the worldwide web made it much easier to share and receive the information. Search engines would be the ones to help us find the target information conveniently. The famous Google was also founded by the search engine. The searching process is always depends on the characteristics of the web pages, for example, entropy is one of the characteristics index. The target web pages could be found by combining the index with the keywords information given by user. Or in other words, it is to find out the web pages which are the most similar to the user’s demands. In biology and ecology, similarity index function is commonly used for classification problems. But in practice, the pairwise instead of single similarity would be obtained to check if two communities are similar or not. It is dislike the thinking of search engines. This research is to find out which has better classification result between single index and pairwise index for the data which is multinomial distributed, especially distributed like a geometry distribution. This data assumption is often satisfied in ecology area. The following classification methods would be considered into this research: single index including entropy and Simpson index, pairwise index including pairwise entropy and similarity index (Yue and Clayton, 2005), and also support vector machine and logistic regression. Computer simulations and cross validations would also be considered here. In this research, it is found that the single index, entropy, has good classification result than imagine. Sometime using entropy to classify would even better than using support vector machine with raw data. But using entropy to classify is not very robust, it is the one needed to be improved in future.

在全球化市場的激烈競爭環境中，資訊科技對企業而言已是一種提升競爭優勢的策略性設備，而先前文獻對於資訊科技能力的評估或與企業績效關聯性的探討，多以行為性問卷的認知數據量表進行研究，少有利用事實性問卷所收集的現象數據評估資訊科技能力及進一步分析資訊科技能力與企業績效關聯性之研究。故本研究基於資源基礎觀點理論，利用企業事實性現象填答問卷建立企業資訊科技能力評估模型，包含資訊科技的導入狀態、應用方式及使用經驗等現象相關問項，如硬體、網路、資訊系統應用程度及範圍等，並探討資訊科技能力與企業績效的關聯性。使用典型相關分析進行實證研究發現，針對先前政府委託調查所收集資料計算出來的企業資訊科技能力，與公開發行的上市櫃企業財務資料所計算出來的企業績效具有顯著關聯性，特別是會計型財務績效之經營能力，經檢定具統計顯著性。進一步進行產業別比較，先使用灰色熵權重分析對於各個子構面進行權重估計，並以權重加權法重新計算每一樣本之資訊科技能力，再進行單因子變異數分析，顯示各產業間之資訊科技能力及子構面能力多數呈現顯著差異。本研究所提出的資訊科技能力評估模型與企業績效關聯檢定模式，以及產業間資訊科技能力差異性分析模式，可提供政府或產業觀察機構建立長期觀測平台，以彙整各種產業資訊科技導入現象及應用範圍，使政府與企業可檢視整體產業整體或個別產業資訊科技能力之差異，藉以擬定資訊科技投資策略，提升企業競爭優勢。 / In the highly competitive globalization environment, information technology (IT) has become strategic equipment for leveraging a business’s competitive advantage. Most previous studies use perceptual questionnaire to collect behavioral data for evaluating IT capability, and furthermore to explore the relationship between IT capability and firm performance. Very few studies use factual questionnaire to collect the phenomenon data for analysis. In this study, we propose a model of evaluating IT capability based on Resource-Based View (RBV) theory and use factual phenomenon questionnaire including induction status, application approach, and usage experience, such as hardware, networks, IS application levels and scopes, etc. The research also explores the relationship between IT capability and firm performance. The IT capability data are calculated from the earlier government-sponsored survey. The firm performance data by financial indicators are collected or calculated from the open data of listed companies in Taiwan Stock Exchange and Over-the-Counter Agencies. The Canonical Correlation Analysis is used and shows significantly positive relationship for the IT capability affecting the firm performance, especially in Accounting-Based Financial Indicators. Before further analysis of industry comparison, Grey Entropy is used to estimate the weights of three sub-constructs and the overall IT capability is then re-calculated by integrating the weighted sub-construct capabilities. Afterwards, the One-Way ANOVA analysis is conducted and shows significant differences across industries in the overall IT capability of the firm and the IT capabilities of the sub-constructs. The proposed IT capability estimation model and the relationship analysis for the IT capability and firm performance can be used by the government or industry observation institution to continuously watch the industry IT capability phenomena and its relationship with the firm performance. The observation for the whole country and across industries can be used as a reference to pursue appropriate IT investments for strategic advantage.

時間數列分析過程，常常發現其走勢，隨著時間過程而演變，應用傳統的線性模式來配適，往往很難獲得合適預測值。因此近幾年來，非線性時間數列結構性改變的研究越來越受到重視，也一直是時間數列及計量經濟學學者所熱衷的研究主題之一。本文利用模糊理論的觀念，以模糊炳找出配適ARCH模式數列之轉折區間，分別以轉折區問起始點及結束點作為截取點，去配適ARCH(1)模式，稱之為截取式自迴歸條件變異數分析法(Trimmed ARCH(1) model)。針對台幣對美元銀行間每日收盤匯率，分別以單變量ARIMA、ARCH(1)、Trtmmed ARCH(1)來建構模式，並做比較分析。比較結果發現，以轉折區間結束點作為截取點之Trimmed ARCH(1)模式，其預測值最為準確，大為改善了原來ARCH(1)模式之預測水準。 / In time series analysis, we often find the trend of which changing with time. Using the traditional model fitting can't get a good prediction. Hence the research of structure change of non-linear time series is attentive in recent years, and non-linear time series analysis is a research topic which the scholars of time series and econometrics are intent on. This article tries to use the theory of fuzzy ,to recognize the structure change period by the fuzzy classification, let the first point and the last point of the structure change period be the cute points, to fit ARCH(1) mod ie which we called the Trimmed ARCH(1) model. We use the data of the exchange rate between N.T dol liars and U.S dollars to compare the ARIMAwith ARCH(1) and Trimmed ARCH(1), the forcasting performance shows that Trimmed ARCH(1) model takes a better prediction result.

使用母數統計方法（Parametric Tests）分析資料時，常需滿足常態假設，但實際得到的資料卻少有常態，因此研究違反常態假設對統計量所造成影響的強韌性研究（Robustness Research）在應用統計方法上是重要的研究主題。在進行此類研究時，常使用蒙地卡羅法（Monte Carlo Method）產生非常態之資料進一步進行研究，目前雖已有多個可產生非常態連續資料的方法被提出，但心理學研究之資 料卻多為間斷資料。而在產生非常態間斷資料時，除難以產生指定參數之間斷分配外，亦有無限多組具同樣參數之間斷分配可供選擇。針對以上兩困難，本研究提出可使用最大資訊熵程序估計符合指定參數之單變數間斷分配，用以產生對應之單變數間斷資料。最大資訊熵方法可所估出之間斷最大資訊熵分配除為符合指定參數時最常出現之分配以外，同時具有平滑、非必要無0 機率等特性。本研究呈現指定4 參數（平均數、變異數、偏態及峰度）與指定2 參數（偏態及峰度） 之最大資訊熵方法，及相對應之R 套件，並以R 套件對此2 方法進行探討評估。結果發現本研究所提出之二方法，在要求指定參數與估計參數之誤差均不超過 .001 時，均可估計出符合指定參數之可能組合之分配，顯示此二方法可精確產生指定參數之間斷分配。而本研究所提供之R 套件，除可在輸入點數、指定參數後產生間斷分配，亦可輸入指定樣本數目及樣本數於此間斷分配中抽取樣本，使此二方法於使用蒙地卡羅法進行間斷資料之強韌性研究時，更易於使用。 / When conducting the robustness researches about normality assumption with Monte Carlo method, a procedure for simulating non-normal data is needed. Some procedures for simulating the non-normal continuous data have been proposed, but the discrete data of ordered categorized variables (e.g., Likert-Type scale) are what we met mostly in practice. To estimate the discrete probability distribution precisely and choose one from infinite discrete probability distributions with the same constraints are 2 difficulties encountered on discrete data simulating process. Therefore, the research purposed a procedure called Maximum Entropy Procedure (MEP) which simulates the univariate discrete maximum entropy distribution with the specified parameters. The distribution is the one with greatest number with the specified parameters, most unlikely probability distribution with 0 probability and smoothest. The characteristics make the MEP a reasonable and considerable choice on simulating univariate discrete data with specified parameters. The MEP-4 (constraints on mean, variance, skewness and kurtosis), the MEP-2 (constraints on skewness and kurtosis) and the corresponding R packages which could estimate the univariate discrete distributions with the specified parameters are presented, evaluated and discussed in this research. It shows that the MEP-4 and MEP-2 are able to estimate the discrete probability distributions precisely with possible combinations of specified parameters with all differences are smaller than .001 and thus useful for robustness researches. The R packages presented in this study are easily to estimate the discrete probability distributions with specified parameters and generate data from these distributions with specified number of samples and sample size. Therefore the MEP-4 and MEP-2 could be easily implemented for generating discrete data with the specified parameters through the corresponding R package and thus useful for Monte Carlo method of robustness researches.

資料分類的應用在時間數列的分析與預測過程相當重要。而模糊資料近年來更受到重視，其應用的範圍包含：財金、社會、生醫、電機等各個領域。本研究欲運用模糊資料分類法，對區間時間數列的轉折偵測與模式建構做一個深入探討。主要應用平均累加模糊熵(average of the sum of fuzzy entropies)， 找出其結構性改變的區間。並針對區間型時間數列進行模式建構診斷與預測。最後我們以單身人口數與失業率為實列做一個詳細的探討。結果顯示，失業率對單身人口數有顯著的影響而孤鸞年的效應並不顯著。 / The application of data classifications in time series analysis and forecasting is rather important. The fuzzy data classification has received much attention recently. It can be applied on various fields such as finance, sociology, biomedicine, electrical engineering and so on. This study is to use the fuzzy data classification to perform an intensive research on the change periods detection and model construction of the interval time series. We use average of the sum of fuzzy entropies to find out interval of the structural changes. Focusing on the time series of intervals, we build a model and make prediction about it. At the end, based on the case study on the population of singles versus, we thoroughly discuss this topic. The result shows that the unemployment rate does significantly correlate with the population of singles, but the "widow's year" does not .