假設二元反應曲線為簡單線性羅吉斯迴歸模型(Simple Linear Logistic Regression Model)，在樣本數為偶數的前題下，所謂的最佳D型設計(D-Optimal Design)是直接將半數的樣本點配置在第17.6個百分位數，而另一半則配置在第82.4個百分位數。很遺憾的是，這兩個位置在參數未知的情況下是無法決定的，因此逐次實驗設計法(Sequential Experimental Designs)在應用上就有其必要性。在大樣本的情況下，本文所探討的逐次實驗設計法在理論上具有良好的漸近最佳D型性質(Asymptotic D-Optimality)。尤其重要的是，這些特性並不會因為起始階段的配置不盡理想而消失，影響的只是收斂的快慢而已。但是在實際應用上，這些大樣本的理想性質卻不是我們關注的焦點。實驗步驟收斂速度的快慢，在小樣本的考慮下有決定性的重要性。基於這樣的考量，本文將提出三種起始階段設計的方法並透過模擬比較它們之間的優劣性。 / The D-optimal design is well known to be a two-point design for the simple linear logistic regression function model. Specif-ically , one half of the design points are allocated at the 17.6- th percentile, and the other half at the 82.4-th percentile. Since the locations of the two design points depend on the unknown parameters, the actual 2-locations can not be obtained. In order to dilemma, a sequential design is somehow necessary in practice. Sequential designs disscused in this context have some good properties that would not disappear even the initial stgae is not good enough under large sample size. The speed of converges of the sequential designs is influenced by the initial stage imposed under small sample size. Based on this, three initial stages will be provided in this study and will be compared through simulation conducted by C++ language.

隨著國人持信用卡消費購物方式的蔚為風氣，致使發卡機構每日所需處理 的申請案件激增；同時，由於信用卡業務的日趨多元化，更增添了審核的 複雜度。傳統以人為判斷為主的審核方法，在有限的人力之下，勢將難以 因應如此龐大的審核需求，而在時間緊迫、經驗累積不足的情形下，難免 會危及授信品質，而增加了此項授信業務的風險。有鑑於此，本研究希望 能藉由範例學習法建立一信用卡信用風險審核模式，期能有效輔助信用卡 發卡審核作業，以降低授信風險，並提昇發卡機構的經營績效。本研究以 某發卡機構為研究對象。抽取個案機構81全年度，審核通過的資料作為研 究樣本。其中以截至82年度3月止，被強制停卡者之不良卡戶，計2,788筆 ；而仍繼續流通的正常卡戶，計有97,001筆，總計99,789筆，作為系統學 習及測試所需之資料。本研究首先針對信用卡審核問題的特性，探討範例 學習法的處理策略，我們將計質線索以相對風險的觀念轉換為順序尺度， 並使所建構的二元樹之葉節點（判斷法則）精簡了二分之一左右，和原分 類樹預測能力並無顯著差異。其次，我們進一步運用修剪策略，可將原判 斷法則數由230條減至26條，大幅的提升了執行效率；修剪策略的運用， 雖然降低了區別率，但卻將預測能力（命中率）由67.1%提升至72.58%。 亦即研究結果顯示，避免分類過細，有助於系統預測能力的提升。本研究 範例學習審核模式之預測能力達72.58%，較 Logistic Re- gression 審 核模式高出約6.49%。在重要性線索的選取上，二者具有相當的一致性； 研究結果顯示，原持有一般卡張數、金卡張數、教育程度、公司等級、職 級等為區別力較佳的信用要素。其中區別能力最強的因素為原一般卡持有 張數，張數愈多，信用風險愈高，而此因素為原審核模式所疏漏者，值得 授信人員警惕。此外，我們再將成本效益因素加入分類樹判斷法則，透過 此方式可調整授信的門檻，以增加發卡機構所能獲取的利潤。進一步考量 申請者的信用風險與所得，建立一信用額度核定的方式。研究結果顯示， 以此一模式授與信用額度較原機構之授與方式,高出約一仟二佰萬元淨收 益。此乃由於本模式能有效辨識出不良客戶（命中率為 80.58%），因而 大幅地降低了呆帳的損失。最後，我們綜合本研究的心得，提出一些未來 研究課題，期能使最適分類樹的產生更具效率，並且擴大研究的範圍，希 望能將信用卡範例學習系統推展至各發卡機構，並應用於信用管理的各層 面，以有效提昇信用卡經營效益。

IPCC於第四次報告書中，模擬氣候持續暖化預測未來海平面上升之情形，而海平面上升對於人口集中的台北都會區將產生莫大之衝擊，同時亦會造成土地利用變遷。本研究目的在於探討海平面上升對台北都會區土地利用變遷之影響，以及受淹沒地區之人口未來可能之遷移區位，主要係從社會經濟發展層面進行分析。運用二元羅吉斯迴歸模式並結合土地利用轉換與影響模型（CLUE-s），以四種不同情境模擬土地利用變遷之情況，其情境分別為海平面上升59公分管制農林地與不管制，以及海平面上升100公分管制農林地與不管制。 　　研究結果顯示，海平面上升之情形越嚴重，未來的都市建地發展會越趨向擴張的形式，對農林地產生變遷之壓力；而於同一海平面上升水平下，針對農林地進行管制，其建地之發展會較為集中於都市計畫地區範圍內，且多數人口亦將遷移至此，容易形成人口壓力。此外，土地利用變遷模擬所得之新增建地部分，於情境A、C下足以容納受影響之人口，但情境B、D則不足以容納，皆會對台北都會區產生容受力之問題，產生累計人口超過計畫人口之現象。因此，建議未來政府於都市發展上，可調整都市計畫內之土地使用強度或都市範圍，也可利用都市更新之方式減少新的土地開發；此外，從國家層級分析，未來的國土計畫法於立法上，亦可將更多氣候變遷相關影響因子納入四大功能分區，透過適宜性分析進行分類分級，以做為未來土地利用發展之引導。 / The 4th IPCC report simulated climate warming and predicted future sea-level rise scenarios. A sea level rise will have a great impact on the population of the Taipei metropolitan area, and it will also produce land-use changes. The purpose of this study is to research the impact of sea level change on land use changes and population migration in Taipei metropolitan, and the areas that will be immigrated by the flooded areas. The method used includes binary logistic regression model combined with Conversion of Land Use and its Effects Model (CLUE-s). Setting the sea-level rise and regulating agricultural land, forest land of the four simulated situations. The results show that the higher sea level rises, the more transformation towards urban development in the future, and the higher sea level will also pressure agriculture and forestry changes. As a result of population pressure on Taipei metropolitan area, the same level of sea-level rise for the regulation of agriculture and forestry land, the development of building sites and population migration will be more concentrated on urban planning areas. In addition, the simulation of land use change generated new building sites, sufficient enough to accommodate the affected population under A and C simulated situations, but B and D couldn’t generate such results. Instead, it will generate a carrying capacity issue. Therefore, this thesis suggests that the government adjust the intensity of land use in urban development, urban areas and urban renewal of urban planning. In addition, the analysis from the national level, the future legislation of Land Planning Act should include the relation of climate change impact factors as a consideration of future land use development.

本研究同時探討房貸違約與提前清償終止行為，並對提前清償動機『出售』與『轉貸』予兼容並蓄。蒐集自H銀行房貸資料，運用多項式邏吉斯廻歸模型分析法，以總體經濟因子之有無分兩階段，對應變數-房貸終止行為(Y1)及提前清償動機(Y2)，採取雙層次深入實證。 實證結果：從機率模型配適度與解釋力之實證分析中，獲得一致結論，對於含有總體經濟因子-經濟成長率與CPI 年增率之模型，其所計算Cox & Snell及Nagelkerke之假 值分別為0.534及0.611，均大於0.5以上，其相對性與獨立性上，均具備較高的模型解釋力，為一較優之房貸違約與提前清償終止行為模型，其相關顯著影響因子如下： 一、對違約具有顯著影響之因子，包括：借款年限、初貸利率、LTV、保證人、次級房貸、部分提前清償、購屋投資、 PTI、DBR＞22、軍警公教人員、設二胎、區域台北市、區域新北市及CPI年增率等14項。 二、對提前清償具有顯著影響之因子，包括：初貸利率、次級房貸、部分提前清償、購屋投資、購屋自住、年資、設二胎、CPI年增率及經濟成長率等9項。 三、對提前清償動機-『出售』之顯著因子：次級房貸、部分提前清償、購屋投資、設二胎等4項。 四、對提前清償動機-『轉貸』之顯著因子：初貸利率、次級房貸、購屋自住、年資、設二胎、CPI年增率及經濟成長率等7項。。 本實證結果所建構含有總體經濟因子之房貸終止行為機率模型，以其顯著影響因子再深入探討提前清償-『出售』與『轉貸』之動機，使提前清償行為模式更精確性地呈現，有助於提供金融機構，在計算違約機率與提前清償機率之模型架構上及授信審核評估上，具貢獻價值之參考資訊。 / This research explores the default and prepayment on the termination behaviors of residential mortgages, as well as the motives of prepayment in house selling or refinancing. Mortgage data are collected from the H bank in Taiwan, using the multi-nominal logistic regression analytic method to explore the factors affecting the default and prepayment behaviors. Empirical results show that loan period, initial mortgage rate, loan-to-value ratio (LTV), the existence of the guarantor, subprime mortgages, curtailment, investment purpose, payment-to-income (PTI), debt burden ratio exceeded 22 (DBR＞22), government employees or teachers, second lien, location in Taipei city, the new Taipei city and CPI annual rate of increase are the significant factors of default behavior. As for the variables on prepayment, the initial mortgage rate, subprime mortgages, curtailment, investment purpose, residential use, DBR＞22, the age of professional career, second lien, CPI annual rate of increase and economical growth rate are significant. For more detailed curtailment behavior, the empirical results show that house sale subprime mortgages, curtailment, residential investment purpose, second lien are significant factors. As for the variables in inter-bank refinancing initial interest rate, subprime mortgages, residential investment purpose, the age of professional career, second lien, CPI annual rate of increase and economical growth rate are significant factors. Results of this research may provide financial institutions precious references on the mortgage default and prepayment behaviors. The mortgage industry can take into account of the significant results on the capital planning in the future.

<p>This study formed part of a larger project that is concerned with the adaptation of a test of cognitive academic language proficiency, the Woodcock Mu&ntilde / oz Language Survey (WMLS). The WMLS has been adapted from English into isiXhosa and the present study is located within the broader study that is concerned with establishing overall equivalence between the two language versions of the WMLS. It was primarily concerned with the Verbal Analogies (VA) scale. Previous research on this scale has demonstrated promising results, but continues to find evidence of some inequivalence. This study aimed to cross-validate previous research on the two language versions of the WMLS and improve on methodological issues by employing matched groups. It drew upon an existing dataset from the larger research project. The study employed a monolingual matched two-group design consisting of 150 mainly English speaking and 149 mainly isiXhosa learners in grades 6 and 7. This study had two sub aims. The first was to investigate item bias by identifying DIF items in the VA scale across the isiXhosa and English by conducting a logistic regression and Mantel-Haenszel procedure. Five items were identified by both techniques as DIF. The second sub aim was to evaluate construct equivalence between the isiXhosa and English versions of the WMLS on the VA scale by conducting a factor analysis on the tests after removal of DIF items. Two factors were requested during the factor analysis. The first factor displayed significant loadings across both language versions and was identified as a stable factor. This was confirmed by the Tucker&rsquo / s Phi and scatter plot. The second factor was stable for the English version but not for the isiXhosa version. The Tucker&rsquo / s phi and scatter plot indicated that this factor is not structurally equivalent across the two language versions</p>

In this work we address to the problems of the loan origination decision-making systems. In accordance with the basic principles of the loan origination process we considered the main rules of a clients parameters estimation, a change-point problem for the given data and a disorder moment detection problem for the real-time observations. In the first part of the work the main principles of the parameters estimation are given. Also the change-point problem is considered for the given sample in the discrete and continuous time with using the Maximum likelihood method. In the second part of the work the disorder moment detection problem for the real-time observations is considered as a disorder problem for a non-homogeneous Poisson process. The corresponding optimal stopping problem is reduced to the free-boundary problem with a complete analytical solution for the case when the intensity of defaults increases. Thereafter a scheme of the real time detection of a disorder moment is given.

Background: When assessing the association between a factor X and a subsequent outcome Y in observational studies, the question that arises is what are the variables to adjust for to reduce bias due to confounding for causal inference on the effect of X on Y. Disregarding such factors is often a source of overestimation because these variables may affect both X and Y. On the other hand, adjustment for such variables can also be a source of underestimation because such variables may be the causal consequence of X and part of the mechanism that leads from X to Y. Methods: In this paper, we present a simple method to compute control variables in the presence of age at onset data on both X and a set of other variables. Using these age at onset data, control variables are computed that adjust only for conditions that occur prior to X. This strategy can be used in prospective as well as in survival analysis. Our method is motivated by an argument based on the counterfactual model of a causal effect. Results: The procedure is exemplified by examining of the relation between panic attack and the subsequent incidence of MDD. Conclusions: The results reveal that the adjustment for all other variables, irrespective of their temporal relation to X, can yield a false negative result (despite unconsidered confounders and other sources of bias).

Bakgrund: Tidigare studier har identifierat flera riskfaktorer, såsom kost, fysisk aktivitet och BMI, gällande kolorektal cancer. Att analysera kost utifrån kostmönster istället för enskilda livsmedel har visat sig vara effektivt för att undersöka risker för kolorektal cancer. Datamaterial samlades in med hjälp av en fall-kontroll studie med 257 fall och 805 kontroller. Syfte: Identifiera faktorer som ger en höjd eller sänkt risk för kolorektal cancer utifrån områdena kost, fysisk aktivitet och BMI. Metod: Faktoranalys användes för att upptäcka kostmönster. Logistisk regression användes för att skatta oddskvoter och 95 % konfidensintervall. Resultat: Tio stycken kostmönster erhölls från faktoranalysen. Kostmönstren ”Läsk, juice och mjölkprodukter” (OR=1,288; ORQ4=2,159), ”Te, men inte kaffe”(OR=1,228; ORQ3=1,891; ORQ4=1,668) och ”Fågel, rött kött och fisk”( ORQ4=1,724) gav alla en ökad risk. Däremot visade kostmönstret ”Mat från säd och ost”( ORQ2=0,546; ORQ4=0,592) en minskad risk. BMI för tio år sedan (OR=1,079; ORÖvervikt=1,491; ORFetma=2,260) identifierades som en riskfaktor. Att arbeta inom stillasittande (OR=0,975; OR&gt;15 år=0,517) och mellanaktiva (OR=0,977; OR6-10 år=0,497;OR&gt;15 år=0,565) yrken visade på en minskad risk. Slutsats: Flera kostmönster visade sig vara riskfaktorer, detta gäller även BMI för tio år sedan. Kostmönstret ”Mat från säd och ost” och att arbeta i fysiskt lätta till medeltunga yrken visade sig vara skyddande faktorer. / Background: Previous studies have shown several risk factors for developing colorectal cancer such as diet, physical activity and BMI. The method of analyzing diets based on dietary patterns, rather than individual food items, have been shown to be effective when investigating the colorectal cancer risk. The data was collected using a case-control study of 257 cases and 805 controls. Aim: Identify factors that cause increased or decreased risk in developing colorectal cancer based on diet, physical activity and BMI. Methods: Factor analysis was conducted to identify dietary patterns. Logistic regression was used to estimate odds ratio and 95 % confidence interval. Results: Factor analysis conducted ten dietary patterns, three of these patterns showed an increased risk “Soft drinks, juice and milk products” (OR=1,288; ORQ4=2,159), “Tea, but not coffee” (OR=1,228; ORQ3=1,891; ORQ4=1,668) and “Poultry, red meats and fish” (ORQ4=1,724).The dietary pattern “Food based on grain and cheese” (ORQ2=0,546; ORQ4=0,592) showed a decreased risk. BMI ten years ago (OR=1,079; OROverweight=1,491; ORObese=2,260) identified as a risk factor. To work in sedentary (OR=0,975; OR&gt;15 years=0,517) or physically medium heavy (OR=0,977; OR6-10 years=0,497; OR&gt;15 years=0,565) occupations indicated a decreased risk. Conclusions: Several dietary patterns has been identified as risk factors, this also includes BMI ten years ago. The dietary pattern “Food based on grain and cheese” and to work in sedentary or physically medium heavy occupations proved to be protective factors.

Over the past 100–150 years Texas rangelands have dramatically changed from native open savannahs to dense woodlands. On the Edwards plateau, a major management concern is the increasing encroachment of Ashe juniper (Juniperus ashei). Preceding an anticipated brush management program, I investigated the presence, co-occurrence, and habitat relationships of 7 breeding bird species in the Leon River Watershed in central Texas, USA: black-capped vireo (Vireo atricapillus), golden-cheeked warbler (Dendroica chrysoparia), northern bobwhite (Colinus virginianus), white-eyed vireo (Vireo griseus), Bell’s vireo (Vireo bellii), painted bunting (Passerina ciris), and brown-headed cowbird (Molothrus ater). Vegetation characteristics were compared between sites occupied by each species and unoccupied sites using univariate analysis. Models for predicting species site occupancy were developed (using logistic regression) based on habitat characteristics correlated with the presence of each species. Two species of special concern, the endangered black-capped vireo and golden-cheeked warbler occupied 5.6% of sites and 13.8% of sites respectively, while the brood parasite brown-headed cowbird was the most widespread, occupying 86.8% of sites. Species co-occurrence patterns revealed significant associations between the golden-cheeked warbler and each of 5 other species. For most species, variables included in habitat models could be explained by knowledge of species known habitat associations. For example, the black-capped vireo was positively associated with increasing low-growing (<1.5 m) hardwood cover and with Low Stony Hill ecological sites. The golden-cheeked warbler was positively associated with increasing density of larger juniper trees, increasing variability in vertical vegetation structure, and decreasing midstory canopy of deciduous nonoaks (e.g., cedar elm [Ulmus crasifolia]). It also preferred Low Stony Hill and Steep Adobe ecological sites. Site occupancy seemed to be driven by variables that describe overall vegetation structure. In particular, cover of low-growing non-juniper vegetation and juniper tree density appeared to be important in determining site occupancy for several species. Although the models constructed were not very robust, resource managers can still benefit from such models because they provide a preliminary examination of important controlling variables. Managing rangelands to maintain or restore a mosaic of juniper patches and open shrublands are likely to help meet the habitat requirements of these bird communities.

Managed lanes are a form of congestion pricing that use occupancy and toll payment requirements to utilize capacity more efficiently. How socio-spatial characteristics impact users’ travel behavior toward managed lanes is the main research question of this study. This research is a case study of the conversion of a High Occupancy Vehicle (HOV) lane to a High Occupancy Toll (HOT) lane, implemented in Atlanta I-85 on 2011. To minimize the cost and maximize the size of the collected data, an innovative and cost-effective modeling framework for socioeconomic analysis of managed lanes has been developed. Instead of surveys, this research is based on the observation of one and a half million license plates, matched to household locations, collected over a two-year study period. Purchased marketing data, which include detailed household socioeconomic characteristics, supplemented the household corridor usage information derived from license plate observations. Generalized linear models have been used to link users’ travel behavior to socioeconomic attributes. Furthermore, GIS raster analysis methods have been utilized to visualize and quantify the impact of the HOV-to-HOT conversion on the corridor commutershed. At the local level, this study conducted a comprehensive socio-spatial analysis of the Atlanta I-85 HOV to HOT conversion. At the general scale, this study enhances managed lanes’ travel demand models with respect to users’ characteristics and introduces a comprehensive modeling framework for the socioeconomic analysis of managed lanes. The methods developed through this research will inform future Traffic and Revenue Studies and help to better predict the socio-spatial characteristics of the target market.