逆物流(reverse logistics)代表了將使用過的產品從消費者手上收回、並將此資源重新在市場上再利用的一連串物流活動。其配送成本往往比正向物流高，對於回送之產品，在運送、儲存、處理、管理方面亦無規律通路，較正向供應鏈增加許多的複雜性和不確定性，企業往往選擇將逆向物流之活動外包給專業物流服務商。 / 對逆向物流服務商來說，既以營利為目標，便有營運範疇內法規、利潤、運輸成本、營運成之考量。過去逆向物流方面之研究主題，多以逆向供應鏈上的廠址設置為主，本研究針對同時具有多個處理廠的逆物流服務供應商進行探討，建立適合的營運模式，考慮多時期、多個逆物流處理廠、多種型態的退回商品，建立一數量決策模式，以逆物流服務商的最大營運利潤為目標，探討逆物流之下的最適合再生物料接受訂單數量、以及個別逆物流處理中心之最適合當期處理量，考慮可能因退回商品回收量之不確定性、處理產出比率的不確定性影響處理廠之中再生物料的實際產量。對於模式當中的不確定因子，本研究建構以情境為基礎的穩健最佳化之模式求得穩健解。 / Reverse logistics reflects a serial of activities including collecting return products from consumers, recycling, reusing, and reducing the amount of materials used. Implementing reverse logistics is complicated and costs more than forward logistics to a firm. Furthermore, there is not a regular way to handle those transportation, storage, processing and management process. In order to reduce cost and focus on core business, industries choose to outsource those processes to third-party reverse logistics provider. / Previous literatures used to focus on the topic of facility location allocation or designing the infrastructure of reverse logistics distribution channels. From a reverse logistics provider perspective, this research concerned about the operational profit of the reverse logistics service provider who has multiple collection sites and refurbishing processing facilities. This research attempts to maximum the net-profit and presents a multi-period, multiple processing facilities, and multi-type return products to optimize the solution of the quantity of processing return products in each refurbishing processing facilities and the quantity of used material ordered by industries. The formulation uses a scenario-based robust optimization approach to solve those uncertainty factors such as the volume of product collection, the usage rate of return product in this model.

經濟理論明白揭示，在不確定下，金融性資產的選擇不僅要考慮其未來報 酬率的平均值，更需將風險程度納入決策過程中。而最佳風險測度為預測 誤差的變異數(Variance of Forec ast Error)。傳統實証方法均視變異 數為固定常數，實無法掌握變異數具有條件異質性的特點。為了到達此目 的，Engle(1982) 提出向量自迴歸條件異質變異數(ARCH)模型，此模型假 定條件變異數不再是固定常數而是過去干擾項平方的線型函數，為實証方 法上一項偉大的突破。在考慮多個變數的聯立動態體系中，由於跨方程式 間可以互相提供額外的訊息，往往可以增加估計的效率性，直覺上比單變 數的設定更能掌握資料的實際情形。故往後的學者便提出了多元自迴歸條 件異質變異數(Multivariate ARCH) 模型，此一模型亦有其缺點存在，因 其待估計參數過多，形成自由度嚴重減少，將導致估計值缺乏效率性。所 以如何利用可獲得的有限資料對模型進行更有效率的估計方式，此為研究 Multivaria te ARCH的重要課題。本文將對Multivariate ARCH做一系列 的介紹，並利用VAR 的貝氏方法對參數進行估計。而多元因素AR CH模型 也是探討的重點。

依時間順序出現之一連串觀測值，通常會呈現某一型態，而根據所產生的 型態可以作為判斷事件發生的基礎。例如，震波形成原因的判斷﹔追查環 境污染源﹔以及在醫學方面，辨識一個正常人心電圖的型態與患有心臟病 的病人其心電圖的型態…等。對於這些問題，傳統之辨識方法常因前提假 設的限制而失去其準確性。在本文中，我們應用神經網路中的逆向傳播演 算法則來訓練網路，並利用此受過訓練的網路來辨別線性時間數列ARIMA 及非線性時間數列 BL(1,0,1,1)。結果發現，網路對於模擬資料中雙線性 係數介於0.2至$0.8$之間的資料有高達$80\%$以上的辨識能力。而在實例 研究中，我們訓練網路來判斷震波形成的原因，其正確率亦高達80\%以上 。同時，我們也將神經網路應用在環境保護方面，訓練網路來判斷二地區 空氣品質的型態。 / A series of observations indexed in time often produces a pattern that may form a basis for discriminatingetween different classes of events. For instance, in theeology, what are the causes of seismic waves? a earthquakesr the nuclear explosions ?in the eathenics, we can use theethod to inquire the source which pollutes the air in somelace, and in the medicine, to distinguish the difference oflectrocardiograms between a health person and an a patient..ect. In this paper, we utilize the back-propagation to trainnetwork and use of the trained networks to judge the linearRIMA(1,0,0) model between the nonlinear BIL(1,0,1,1) model,e can find that the trained network has a good recognitionhose accurate rate is above 80\% for the coefficient of the bilinear model being equal to 0.5 or 0.8. In a living example, we have trained a network to decidehich is the cause of seismic wave, and the trained networkhose accurate rate is larger than 80\%. At the same time, e also applied neural network in environmental protection.

隨著產品生命週期的縮短，大量的廢棄物對人類生存環境開始造成威脅，各國開始正視廢棄物管理的議題，紛紛立法來規範廢棄物的回收管理。企業為了成本、企業形象、法律規定等因素開始將逆物流(reverse logistic)納入其營運規劃。但在逆物流資訊難以取得以及具備大量的不確定因素下，許多企業選擇將逆物流外包給專業的逆物流業者，以專注在其核心能力之上。 在單純的外包模式下，企業時常面臨回收商無法穩定供給再生物料之問題，若再生物料使用比率無法達到法規要求，則需繳納高額的回收規費，部分企業甚至以新產品分解為新再生物料來補足再生物料短缺以迴避高額的回收規費；回收商則會面臨企業再生物料需求數量不穩定或是數量不足，單次運輸成本大幅提高，使其表現出只願回收退回商品取得回收補貼，但不分解販售再生物料，而是堆放退回商品於集散處的傾向。 本研究提出一綜合回收商及製造商之緊密營運模式，考量連續時期、利潤共享的條件下，建立一數量決策模型，以整體利潤最大化為目標，探討不穩定因素下之每期穩健再生物料分配決策。因應大環境之不穩定因素，將以建構情境為基礎之穩健最佳化模式求得穩健解。 / In recent year, enterprises consider reverse logistic in their processing because of cost, corporate image and government policy. But there are lots of uncertainty factors in the reverse logistic, in order to focus on enterprise’s professional skills, more and more enterprises outsource their reverse logistics. Both enterprise and professional reverse logistic processor have to spend more costs to keep their cooperation in recent outsourcing model. Thus, this thesis builds a model which combine enterprise's business model and professional reverse logistic processor's business model. In this model, assumes that profit should be share between both of them, and apply Robust optimization methods to solve uncertainty factors in reverse logistic. The thesis finds out the best distribution ratio of regeneration materials in each period.

財務報表為制訂授信決策之重要資訊來源，而穩健會計可增加公司財務報表的資訊品質。本研究實證探討當公司採行穩健會計時，是否可降低銀行融資超貸之可能性，使銀行願意為了穩健財務報表的效益，而降低公司需支付的債務融資成本。 本研究以1997至2008年之5,507筆觀察值為研究樣本，依Beaver and Ryan（2005）之分類，將穩健會計衡量指標分為非條件式穩健會計與條件式穩健會計，分析公司會計穩健程度對銀行融資利率的影響，並探討公允價值會計是否會影響條件式穩健會計與銀行融資利率之關聯性。 本研究之主要實證結果顯示，非條件式穩健會計與條件式穩健會計皆與銀行融資利率呈負向關係；公允價值會計之採用與銀行融資利率呈負向關係，且正向影響條件式穩健會計與銀行融資利率之關聯性。非條件式穩健會計與銀行給予融資超貸之風險呈負向關係，但條件式穩健會計與銀行給予融資超貸風險之關聯性並不一致。額外之測試顯示，主要實證發現不因改採其他方式衡量銀行融資利率，或以稅前息前淨利取代稅前息前折舊前淨利衡量超貸風險而有改變。 / Financial statement is one of important resources to credit decisions making. Conservatism increases the quality of financial statement. Based on the definition of types of accounting conservatism, unconditional and conditional, proposed by Beaver and Ryan (2005), and the use of a sample of 5,507 firm-year financial data from 1997 to 2008, this study investigates conservatism effects on the over-loan risks from borrowers through bank loan pricing. The primary empirical findings indicate that a significantly negative association exits between unconditional (conditional) conservatism and bank loan pricing. In addition, a significantly negative association exits between fair-value accounting and bank loan pricing, and the adoption of fair-value accounting affect the association between conditional conservatism and bank loan pricing. Moreover, the result also shows that a significantly negative association exits between unconditional conservatism and the over-loan risks. The analysis of additional tests indicates that the primary findings held when alternative measurements of interest rates are used for proxy variables for bank loan pricing and EBIT is used for proxy variables for EBITDA.

隨著WiFi訊號在都市的涵蓋率逐漸普及，基於WiFi訊號強度值的定位方法逐漸發展。WiFi匹配定位(Matching Positioning)是透過參考點坐標與WiFi訊號強度(Received Signal Strength Indicator, RSSI)的蒐集，以最小二乘法(Least Squares, LS)計算RSSI模型參數；然後，利用模型參數與使用者位置的WiFi訊號強度，推估出使用者的位置。然而WiFi訊號強度容易受到環境因素影響，例如降雨、建物遮蔽、人群擾動等因素，皆會使訊號強度降低，若以受影響的訊號強度進行定位，將使定位成果與真實位置產生偏移。 為了降低訊號強度的錯誤造成定位結果的誤差，本研究嘗試透過具有穩健性的最小一乘法( Least Absolute Deviation, LAD)結合WiFi匹配定位，去克服WiFi訊號易受環境影響的特性，期以獲得較精確的WiFi定位成果。研究首先透過模擬資料的建立，測試不同粗差狀況最小一乘法WiFi匹配定位之表現，最後再以真實WiFi訊號進行匹配定位的演算，並比較最小一乘法WiFi匹配定位與最小二乘法WiFi匹配定位的成果差異，探討二種方法的特性。 根據本研究成果顯示，於模擬資料中，最小一乘法WiFi匹配定位相較於最小二乘法WiFi匹配定位，在面對參考點接收的AP訊號與檢核點接收的AP訊號強度含有粗差的情形皆能有較好的穩健性，且在參考點接收的AP訊號含有粗差的情況有良好的偵錯能力。而於真實環境之下，最小一乘法WiFi匹配定位之精度也較最小二乘法WiFi匹配定位具有穩健性；在室外資料的部份，最小一乘法WiFi匹配定位之精度為8.46公尺，最小二乘法WiFi匹配定位之精度為8.57公尺。在室內資料的部份，最小一乘法WiFi匹配定位之精度為2.20公尺，最小二乘法WiFi匹配定位之精度為2.41公尺。 / Because of the extensive coverage of WiFi signal, the positioning methods by the WiFi signal are proposed. WiFi Matching Positioning is a method of WiFi positioning. By collecting the WiFi signal strength and coordiates of reference points to calculate the signal strength transformation parameters, then, user’s location can be calculated with the LS (Least Squares). However, the WiFi signal strength is easily degraded by the environment. Using the degraded WiFi signal to positioning will produce wrong coordinates. Hence this research tries to use the robustness of LAD (Least Absolute Deviation) combining with WiFi Matching Positioning to overcome the sensibility of WiFi signal strength, expecting to make the result of WiFi positioning more reliable. At first, in order to test the ability of LAD, this research uses simulating data to add different kind of outliers in the database, and checks the performance of LAD WiFi Matching Positioning. Finally, this research uses real data to compare the difference between the results of LAD and LS WiFi Matching Positioning. In the simulating data, the test result shows that LAD WiFi Matching Positioning can not only have better robust ability to deal with the reference and check points AP signal strength error than LS WiFi Matching Positioning but also can detect the outlier in the reference points AP signal strength. In the real data, LAD WiFi Matching Positioning can also have better result. In the outdoor situation, the RMSE (Root Mean Square Error) of LAD WiFi Matching Positioning and LS (Least Squares) WiFi Matching Positioning are 8.46 meters and 8.57 meters respectively. In the indoor situation, the RMSE (Root Mean Square Error) of LAD WiFi Matching Positioning and LS (Least Squares) WiFi Matching Positioning are 2.20 meters and 2.41 meters respectively.

長期性資料(longitudinal data)的最主要特徵是為對多個被觀測個體在不同的時間點上重複測量一個或多個反應變數。而在分析長期性資料的方法中，Laird & Ware(1982)建議以線性混合效果模型(linear mixed effects model，LME)來進行估計分析，此模型方法中，資料可以允許遺失值，並可將受測個體間與個體內的變異分開說明。 另在配適最小平方法(OLS)的迴歸模型中，係數估計經常會受到異常值的影響，而Rousseeuw & Leroy(1987)提出最小消去平方法(least trimmed squares，LTS)的穩健迴歸模型，即是解決最小平方法中對於異常值敏感的問題。 本研究主要針對台灣股票預期報酬之三種模型：資本資產定價模型、特徵模型、因子模型分別以OLS、LTS、LME三種估計方法做配適，並比較配適模型之適當與否，樣本資料為民國七十年七月至九十年六月共252個月516家上市公司股票報酬。實證結果顯示，不論是採用OLS、LTS、LME的估計方法，股票報酬解釋變數：系統風險、公司規模、帳面權益對市值比、SMB、HML皆為股票報酬的顯著解釋因子；而在模型比較方面，不論是配適資本資產定價模型、特徵模型或因子模型，LME都較OLS為較適當配適模型。這顯示了在分析長期性資料時，LME的確是一個較佳的統計分析模型。

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.

在傳統的線性迴歸分析當中，當基本假設不滿足時，有時可考慮變數轉換使得資料能夠比較符合基本假設。在眾多的轉換方法當中，以Box和Cox(1964)所提出的乘冪轉換(Box-Cox power transformation)最為常用，乘冪轉換可將某些複雜的系統轉換成線性常態模式。然而當資料存在離群值(outlier)時，Box-Cox Transformation會受到影響，因此不是一種穩健方法。 在本篇論文當中，我們利用前進演算法(forward search algorithm)求得最小消去平方估計量(Least trimmed squares estimator)，在過程當中估計出穩健的轉換參數。

在迴歸分析中，當資料中存在很多離群值時，偵測的工作變得非常不容易。 在此狀況下，我們無法使用傳統的殘差分析正確地偵測出其是否存在，此現象稱為遮蔽效應(The Masking Effect)。 而為了避免此效應的發生，我們利用最小中位數穩健迴歸估計值(Least Median Squares Estimator)正確地找出這些群集離群值，此估計值擁有最大即50﹪的容離值 (Breakdown point)。 在這篇論文中，用來求出最小中位數穩健迴歸估計值的演算法稱為步進搜尋演算法 (the Forward Search Algorithm)。 結果顯示，我們可以利用此演算法得到的穩健迴歸估計值，很快並有效率的找出資料中的群集離群值；另外，更進一步的結果顯示，我們只需從資料中隨機選取一百次子集，並進行步進搜尋，即可得到概似的穩健迴歸估計值並正確的找出那些群集離群值。 最後，我們利用鐘乳石圖(Stalactite Plot)列出所有被偵測到的離群值。 在多變量資料中，我們若使用Mahalanobis距離也會遭遇到同樣的屏蔽效應。 而此一問題，隨著另一高度穩健估計值的採用，亦可迎刃而解。 此估計值稱為最小體積橢圓體估計值 (Minimum Volume Ellipsoid)，其亦擁有最大即50﹪的容離值。 在此，我們也利用步進搜尋法求出此估計值，並利用鐘乳石圖列出所有被偵測到的離群值。 這篇論文的第二部分則利用變數轉換的技巧將迴歸資料中的殘差項常態化並且加強其等變異的特性以利後續的資料分析。 在步進搜尋進行的過程中，我們觀察分數統計量(Score Statistic)和其他相關診斷統計量的變化。 結果顯示，這些統計量一起提供了有關轉換參數選取豐富的資訊，並且我們亦可從步進搜尋進行的過程中觀察出某些離群值對參數選取的影響。 / Detecting regression outliers is not trivial when there are many of them. The methods of using classical diagnostic plots sometimes fail to detect them. This phenomenon is known as the masking effect. To avoid this, we propose to find out those multiple outliers by using a highly robust regression estimator called the least median squares (LMS) estimator which has maximal breakdown point. The algorithm in search of the LMS estimator is called the forward search algorithm. The estimator found by the forward search is shown to lead to the rapid detection of multiple outliers. Furthermore, the result reveals that 100 repeats of a simple forward search from a random starting subset are shown to provide sufficiently robust parameter estimators to reveal multiple outliers. Finally, those detected outliers are exhibited by the stalactite plot that shows greatly stable pattern of them. Referring to multivariate data, the Mahalanobis distance also suffers from the masking effect that can be remedied by using a highly robust estimator called the minimum volume ellipsoid (MVE) estimator. It can also be found by using the forward search algorithm and it also has maximal breakdown point. The detected outliers are then displayed in the stalactite plot. The second part of this dissertation is the transformation of regression data so that the approximate normality and the homogeneity of the residuals can be achieved. During the process of the forward search, we monitor the quantity of interest called score statistic and some other diagnostic plots. They jointly provide a wealth of information about transformation along with the effect of individual observation on this statistic.