本論文提出追蹤特定目標線的二階段混合整數非線性隨機規劃模型，以建立追蹤目標線的投資組合。藉由引進情境樹(scenario tree)，我們將此類二階段隨機規劃問題，轉換成為等價的非隨機規劃模型。在金融商品的價格波動及交互作用下，所建立的投資組合在經過一段時間後，其追蹤目標線的能力可能會日趨降低，所以本論文亦提出調整投資組合的規劃模型。為符合實務考量，本論文同時考慮交易成本、股票放空的限制，並且加入期貨進行避險。為了反應投資者的預期心理，也引進了選擇權及情境樹。最後，我們使用台灣股票市場、期貨交易市場及台指選擇權市場的資料進行實證研究，亦探討不同成長率設定之目標線與投資比例對於投資組合的影響。 / To construct a portfolio tracking specific target line, this thesis studies how to do it via two-stage stochastic mixed-integer nonlinear model. We introduce scenario tree to convert this stochastic model into an deterministic equivalent model. Under the volatility of price and the interaction of each financial derivatives, the performance of the tracking portfolio may get worse when time elapses, this thesis proposes another mathematical model to rebalance the tracking portfolio. These models consider the transactions cost and the limitation of shorting a stock, and the tracking portfolio will include a futures as a hedge position. To reflect the expectation of investors, we introduce scenario tree and also include a options as a hedge position. Finally, an empirical study will be performed by the data from Taiwan stock market, the futures market and the options market to explore the performance of the proposed models. We will analyze how the different benchmarks settings and invest ratio will affect the value of the tracking portfolio.

従来型の繊維強化プラスチック(FRP)に対し環境負荷の少ない「グリーンコンポジット」の実用的な成形プロセスの確立が切望されている。本研究では、天然繊維およびポリ乳酸を原料とする長繊維強化樹脂(LFT)ペレット製造および射出成形による複合材製造法を対象に、複合材の力学的特性に対する繊維の含有水分や熱劣化の影響、繊維分散の効果等を把握し、高い特性を発揮させるための成形プロセスおよび諸条件を提示した。 / 'Green Composites' have been attracting attention due to their high sustainability and carbon neutrality. This study investigated the preparation process for composites of long jute fiber reinforced polylactic acid by LFT-pellet manufacturing method followed by injection molding. The author explored effect of several factors, such as moisture in fiber, heat decomposition of fiber at processing and the level of fiber dispersion in matrix resin, on mechanical properties of composites. The author eventually proposed the optimized process and operating windows for attaining higher mechanical properties of composites. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

本文之信用風險模型屬於簡約模型(Reduced Form Model)之範疇，以COX過程解釋違約過程，解釋為何企業會發生連帶倒閉的現象。在考慮信用風險後，各期所產生之現金流量變得具不確定性，因此在計算現金流量之現值時，折現因子就必須考慮信用風險溢酬，本文選用信用風險模型中的一大分支-約簡模型，將信用風險量化(包含系統風險及非系統風險)，進而估計出信用價差期間結構；就如同無風險利率期間結構對固定收益商品之重要性，在估計出公司之信用價差期間結構後，即可針對該公司發行之各種商品進行評價分析。本文並以花旗所羅門美邦控股公司為例進行實證，利用公司債理論價格與市價之誤差平方和，求解違約過程之參數估計值及信用價差期間結構；接著，針對花旗所羅門美邦控股公司所發行之連動債券〝TRAGETS〞，進行評價分析並比較考慮信用風險與否是否有助於理論價格與市價之配適。

近來地理資訊系統(GIS)之資料庫受到不同領域的統計學家廣泛的研究，以期建立及分析可描述空間聚集效應及變異之模型，而描述空間相關存活資料之統計模式為公共衛生及流行病學上新興的研究議題。本文擬建立多維度半參數的貝氏階層模型，並結合空間及非空間隨機效應以描述存活資料中的空間變異。此模式將利用多變量條件自回歸(MCAR)模型以檢驗在不同地理區域中是否存有空間聚集效應。而基準風險函數之生成為分析貝氏半參數階層模型的重要步驟，本研究將利用混合Polya樹之方式生成基準風險函數。美國國家癌症研究院之「流行病監測及最終結果」(Surveillance Epidemiology and End Results, SEER)資料庫為目前美國最完整的癌症病人長期追蹤資料，包含癌症病人存活狀況、多重癌症史、居住地區及其他分析所需之個人資料。本文將自此資料庫擷取美國愛荷華州之癌症病人資料為例作實證分析，並以貝氏統計分析中常用之模型比較標準如條件預測指標(CPO)、平均對數擬邊際概似函數值(ALMPL)、離差訊息準則(DIC)分別測試其可靠度。 / The databases of Geographic Information System (GIS) have gained attention among different fields of statisticians to develop and analyze models which account for spatial clustering and variation. There is an emerging interest in modeling spatially correlated survival data in public health and epidemiologic studies. In this article, we develop Bayesian multivariate semiparametric hierarchical models to incorporate both spatially correlated and uncorrelated frailties to answer the question of spatial variation in the survival patterns, and we use multivariate conditionally autoregressive (MCAR) model to detect that whether there exists the spatial cluster across different areas. The baseline hazard function will be modeled semiparametrically using mixtures of finite Polya trees. The SEER (Surveillance Epidemiology and End Results) database from the National Cancer Institute (NCI) provides comprehensive cancer data about patient’s survival time, regional information, and others demographic information. We implement our Bayesian hierarchical spatial models on Iowa cancer data extracted from SEER database. We illustrate how to compute the conditional predictive ordinate (CPO), the average log-marginal pseudo-likelihood (ALMPL), and deviance information criterion (DIC), which are Bayesian criterions for model checking and comparison among competing models.