房屋貸款借款人對於其所負貸款債務的處分有兩種潛在風險行為，分別是提前清償及違約。這兩種借款人風險行為不管是對金融機構的資產管理，或是對近年在財務金融領域的不動產證券化而言，都是相當重要的探討議題，原因在於提前清償及違約帶來了利息收益與現金流量的不確定性，進而影響不動產抵押債權的價值。也就是為貸款承作機構、證券化保證機構及證券投資人帶來風險。 借款人決定提前清償及違約與否，除了與借款人自身特性及貸款條件有關外，尚受到隨時間經過而不斷變動的變數所影響，亦即許多影響因子並非維持在貸款起始點的狀態，而是會在貸款存續期間動態調整。進一步影響借款人行為，而這類變數即為時間相依變數(time –dependent variables，或time-varying variables)。因此，本研究利用便於處理時間相依變數的比例危險模型(Proportional Hazard Model)來分析借款人提前清償及違約風險行為，觀察借款人特徵、房屋型態、貸款條件及總體經濟等變數與借款人風險行為的關係。 實證結果顯示，借款人特徵部分的教育程度對提前清償及違約風險影響最為明顯，教育程度越高，越會提前清償，越低則較會違約。房屋型態則透天厝較非透天厝容易提前清償及違約。貸款條件中的貸款金額及貸款成數皆與違約為正相關，亦即利息負擔越重，借款人違約風險升高。總體經濟方面，借款人對利率變動最為敏感，反映利率代表借款人的資金成本，是驅動借款人提前清償及違約的財務動機與誘因。

Health technology assessment: case study on breast carcinoma treatment in the Czech Republic Bc. Lenka Šlegerová January 4, 2019 Abstract This thesis proposes an original method for assessing total costs of med- ical treatment. It defines the semi-Markov model with four states that are associated with specific costs of the treatment, and not with patients' health statuses. This method is applied to individuals' treatment data drawn from the Czech clinical practice in the treatment of the metastatic HER2+ breast cancer. The aim is to assess the cost-effectiveness of adding medication per- tuzumab to the combination of trastuzumab+docetaxel within first-line therapy and to examine whether using individual data on Czech patients and the economic conditions leads to different results from foreign stud- ies. Furthermore, employing censored data from the clinical practice in the thesis complicates the estimation of patients' overall survival in compari- son to clinical-trials data that form random samples. Therefore, survival functions were not only estimated by the Kaplan-Meier estimator but also using the Cox proportional hazard model and the Accelerated failure time model that both control for the effects of included covariates. The addition of pertuzumab does not result in significantly longer pa- tients'...

Från 1900-talets början har banker och låneinstitut använt nyckeltal som hjälpmedel vid bedömning och kvantifiering av kreditrisk. För dagens investerare är den ekonomiska miljön mer komplicerad än för bara 40 år sedan då teknologin och datoriseringen öppnade upp världens marknader mot varandra. Bedömning av kreditrisk idag kräver effektiv analys av kvantitativa data och modeller som med god träffsäkerhet kan förutse risker. Under 1900-talets andra hälft skedde en snabb utveckling av de verktyg som används för konkursprediktion, från enkla univariata modeller till komplexa data mining-modeller med tusentals observationer. Denna studie undersöker om det är möjligt att prediktera att svenska företag kommer att gå i konkurs och vilka variabler som innehåller relevant information för detta. Metoderna som används är diskriminantanalys, logistisk regression och överlevnadsanalys på 50 aktiva och 50 företag försatta i konkurs. Resultaten visar på en träffsäkerhet mellan 67,5 % och 75 % beroende på vald statistisk metod. Oavsett vald statistisk metod är det möjligt att klassificera företag som konkursmässiga två år innan konkursens inträffande med hjälp av finansiella nyckeltal av typerna lönsamhetsmått och solvensmått. Samhällskostnader reduceras av bättre konkursprediktion med hjälp av finansiella nyckeltal vilka bidrar till ökad förmåga för företag att tillämpa ekonomistyrning med relevanta nyckeltal i form av lager, balanserad vinst, nettoresultat och rörelseresultat. / From the early 1900s, banks and lending institutions have used financial ratios as an aid in the assessment and quantification of credit risk. For today's investors the economic environment is far more complicated than 40 years ago when the technology and computerization opened up the world's markets. Credit risk assessment today requires effective analysis of quantitative data and models that can predict risks with good accuracy. During the second half of the 20th century there was a rapid development of the tools used for bankruptcy prediction. We moved from simple univariate models to complex data mining models with thousands of observations. This study investigates if it’s possible to predict bankruptcy in Swedish limited companies and which variables contain information relevant for this cause. The methods used in the study are discriminant analysis, logistic regression and survival analysis on 50 active and 50 failed companies. The results indicate accuracy between 67.5 % and 75 % depending on the choice of statistical method. Regardless of the selected statistical method used, it’s possible to classify companies as bankrupt two years before the bankruptcy occurs using financial ratios which measures profitability and solvency. Societal costs are reduced by better bankruptcy prediction using financial ratios which contribute to increasing the ability of companies to apply financial management with relevant key ratios in the form of stock , retained earnings , net income and operating income.

Saskatchewan is the only province in Canada to have routinely tested the antimicrobial susceptibility of all provincially reported human cases of campylobacteriosis. From 1999 to 2006, 1378 human Campylobacter species infections were tested for susceptibility at the Saskatchewan Disease Control Laboratory using the Canadian Integrated Program for Antimicrobial Resistance Surveillance panel and minimum inhibitory concentration (MIC) breakpoints. Of these, 1200 were C. jejuni, 129 were C. coli, with the remaining made up of C. lari, C. laridis, C. upsaliensis and undifferentiated Campylobacter species. Campylobacter coli had significantly higher prevalences of ciprofloxacin resistance (CIPr), erythromycin resistance (ERYr), combined CIPr-ERYr resistance and multidrug resistance (to three or greater drug classes) than C. jejuni. Logistic regression models indicated that CIPr in C. jejuni decreased from 1999 to 2004 and subsequently increased in 2005 and 2006. The risk of CIPr was significantly increased in the winter months (January to March) compared to other seasons. A comparison of logistic regression and Cox proportional hazard survival models found that the latter were better able to detect significant temporal trends in CIPr and tetracycline resistance by directly modeling MICs, but that these trends were more difficult to interpret. Scan statistics detected significant spatial clusters of CIPr C. jejuni infections in urban centers (Saskatoon and Regina) and temporal clusters in the winter months; the space-time permutation model did not detect any space-time clusters. Bernoulli scan tests were computationally the fastest for cluster detection, compared to ordinal MIC and multinomial antibiogram models. eBURST analysis of antibiogram patterns showed a marked distinction between case and non-case isolates from the scan statistic clusters. Multilevel logistic regression models detected significant individual and regional contextual risk factors for infection with CIPr C. jejuni. Patients infected in the winter, that were between the ages of 40-45 years of age, that lived in urban regions and that lived in regions of moderately high poultry density had higher risks of a resistant infection. These results advance the epidemiologic knowledge of CIPr C. jejuni in Saskatchewan and provide novel analytical methods for antimicrobial resistance surveillance data in Canada. / Saskatchewan Disease Control Laboratory (Saskatchewan Ministry of Health); Laboratory for Foodborne Zoonoses (Public Health Agency of Canada); Centre for Foodborne, Environmental and Zoonotic Infectious Diseases (Public Health Agency of Canada); Ontario Veterinary College Blake Graham Fellowship