The credit scoring with the help of classification techniques provides to take easy and quick decisions in lending. However, no definite consensus has been reached with regard to the best method for credit scoring and in what conditions the methods performs best. Although a huge range of classification techniques has been used in this area, the logistic regression has been seen an important tool and used very widely in studies. This study aims to examine accuracy and bias properties in parameter estimation of the logistic regression by using Monte Carlo simulations in four aspect which are dimension of the sets, length, the included percentage defaults in data and effect of variables on estimation. Moreover, application of some important statistical and non-statistical methods on Turkish credit default data is provided and the method accuracies are compared for Turkish market. Finally, ratings on the results of best method is done by using receiver operating characteristic curve.

Differential Item Functioning Analyses investigates whether individuals with same ability in different groups also show similar performance on an item. In matching the individuals of the same ability, most of the methodologies use total scores of the tests which are usually constructed to be unidimensional. th purpose of the present study is evaluating the PISA 2003 mathematics literacy items through the use of DIF methodology which uses a multidimensional approach in matching students instead of single total score, improve the matching for DIF analyses. In the study factor structure of the tests will be determeined via both exploratory and confirmatory analyses in a complimentary fashion. then DIF analyses conducted using Logistic regression (LR) and Mantel -Haenszel methods.Analyses showed that the matching criterion improved when multivariate analyses were used. the number of DIF items was decreased when the matching criterion is defined based on multiple criterion scores such as mathematical literacy and problem solving scores or two different mathematical literacy subtest score. In addition, qualitative reviews and examination of the distribution of DIF items by content categories, cognitive demands, item types,item text, visual-spatial factors and linguistic properties of items were analyzed to explain the differential performance. Curriculum, cultural and translation differences were the main criteria for the qualitative analyses of DIF items. The results imply that curriculum and translation differences in items might be causing the DIF across Turkish and English versions of the tests.

房價可反應家戶負擔能力，在家戶遷移決策上扮演重要角色，鑒於國內遷移相關研究多欠缺房價的考慮，本研究採用2009年「住宅需求動向調查」之已購屋者的資料，以二元羅吉特迴歸模型進行實證，分析臺北縣（市）家戶的遷移決策，了解原居住在臺北市（縣）之家戶選擇在臺北市（縣）內遷移或選擇向臺北縣（市）遷移的影響因素。住宅之單價及屋齡對臺北都會區的家戶均為影響遷移決策的關鍵因素。首購之家戶傾向遷往臺北縣，首次購屋者較非首購家戶會考慮住宅的負擔能力得到證實。教育程度及家戶年所得愈高，傾向遷往臺北市，另購屋決策者搜尋時間增加傾向遷往臺北市，顯示臺北市的住宅市場環境不確定性較臺北縣住宅市場高，亦可能是因為家戶進行遷移決策時帶著先前的參考價格偏誤進入市場，從高評價往低評價地區遷移，會減少搜尋行為而有較快的決策速度。本研究有助於了解臺北都會區之間人口流動的情形，作為政府住宅政策的參考依據。 / House price, which is an indicate to household's ability, plays an important role in the mobility decision. However, few research in Taiwan has touched this issue. Therefore, we use the home buyers' data in the greater Taipei area from 2009's "Housing Demand Survey" to convey our empirical study by employing a binary logistic regression model. Empirically, we find that the price and age of house are the key determinants of the household mobility in the greater Taipei area. First-time home buyers tend to move to the Taipei County. Our research confirms that the first-time home buyers consider the price much more than those who have previously acquired a house (or more) of their own. The households with a higher education level or greater pay tend to move to the Taipei City. Thoroughly-researched home buyers prefer to move to Taipei City. This indicates that the uncertainty of the Taipei City’s house market is greater than of the Taipei County. Or this is due to the fact that households tend to enter the market with biased previous prices, when households from higher evaluation location move to lower, they make mobility decision faster. The results are meaningful for the supply of housing market and public facility services.

隨著資訊科技的發展與網際網路的普及，音樂與電影光碟盜版的問題也逐年嚴重，然影音盜版不僅影響權利人團體，影音業者及創作者之生存，亦攸關我國智慧財產之發展，更常成為我國在國際貿易諮商上的重要課題。在種種緣由與現況下，得使國內許多產、官、學、研團體想去研究影音盜版的相關議題，以了解其嚴重程度如何，或有無較客觀合理的指標或評估方式?並進一步研擬有效方法來防範盜版問題進一步惡化，為此上述問題乃是本研究之源起。 目前所有的影音盜版研究，多著重在計算盜版率,探討盜版因素,盜版行為的心理與法制問題，皆還尚未針對影音盜版率，建構出可供學者推論的盜版率機率分配，及其他相關的數量研究，因此，本研究的主要實証方向，乃根據2004年經濟部智慧財產局(intellectual property office ministry of economic affairs,R.O.C)委託政治大學之消費者調查資料，就音樂CD、影音VCD/DVD兩部分，針對筆者有興趣之變項(性別、年齡、有無上網下載等)，(1)分別建構各自的混合分配並了解其分配間的差異與趨勢， (2)探討消費者對盜版行為的態度，(3)了解消費者對喜好的光碟所願付價格之差異，(4)建立盜版率分配的信賴帶，以及(5)針對現有的調查資料進行盜版辨別。 最後，就查緝盜版與維護智慧財產權兩方面，實證分析提供政府相關單位作為參考的依據，以求擬訂周詳且完善的措施來防範日益惡化的盜版問題。 / With the development of computer technology and widespread of internet, the piracy problem goes more serious. The piracy situation makes much influence not only on the rights of international oblige societies but also the growing of the intellectual properties in Taiwan. Moreover, it becomes the rock on the road of international commercial negotiations. Beyond the serious situation in the mean time, more researchers and relevant organizations on the island are trying to pay more attention to this important issue. This research intends to understand several questions: How is the actual situation on the piracy problem? Are there any objective evaluation ways? Are there any effective policies to prevent it from going deeper? These questions lead to this research. In the meantime, most of Audio & Video piracy research emphasized only on calculating the piracy rate, or the reasons, or the relevant psychological and law problems, but few on piracy quantitative studies. Therefore the mainly intention of this research is based on the data from the IPO(Intellectual Property Office Ministry of Economic Affairs, ROC), which is executing by National Chengchi University. As for the two parts concerning music CD and visual VCD/DVD, and the variables those I am highly interested including gender, age, education level, downloading or not. The empirical study results show as below: (1)The piracy rate distribution corresponds with the Mixed Model, that mean that it have been proportionally mixed two degenerate distribution (while X=0 and 100) with the Normal distribution. (2) On the facets of distribution differences and trends analysis, not only music CD and visual VCD/DVD, the results of the research by Mann-Whitney test and Kolmogorov-Smirnov two sample test both reveal the rising tendency of overall piracy rate. The generation of 20~29 years old is the mainly pirate group, moreover, higher education grades group does the more pirating behaviors, and lower income group intends more unauthorized copying conducts. Furthermore, along with the development of internet technology, the infringement behavior is more serious on the network connectors than the non-network downloaders. (3) Under surveying the opinions of consumers about the piracy, regardless of whether music or movies, the deviation is more serious on male than female, under 30-year-old than above, low educated than high, low income than high, pirate than non-pirate, downloaders than non-downloaders. The problem locates not only the lack of the concepts and recognition on the intellectual properties rights, but also the scarce of moral or legal limitations on the unauthorized rebuilding or downloading. But in the other curious facet, although the higher grade educated groups got more equitable standpoints on the piracy discussion, but evidenced depend upon the collected data they are also mainly the group who did the piracy behaviors more. (4) On the price range that a consumer would like to pay for, most of the pirate consumer tends to pay low price to buy the A/V goods, most of the non-pirating consumer group tends to pay general price to buy ones, and no significant difference of these two groups with high price, (5) On the facets of confidence bands on the whole music CD and visual VCD/DVD pirating rate, because of the specialties of pirating data- the higher frequency while the piracy rate values 0 and 100, so that the upper and lower bound reveals at 0 and 100. Futhermore, the confidence bands obtains from the population distribution function, therefore it’s suitable for the goodness-of-fit test. The results met the Kolmogorov-Smirnov one sample test. (6) On the data recognition facets, the logistic regression model of piracy is constructed in this research. Classification from the fitted logistic regression models, the results reveals 107 non-pirate are mis-judged to pirating behaviors, 186 pirating samples are neglected to non-pirate ones, the correct recognition rate goes high of 88 %. Key Words：Piracy Rate, Mixture Models, Mann-Whitney, Kolmogorov-Smirnov, Logistic Regression Model, Nonparametric Statistics.

銀行在面對借款人以不動產申請抵押貸款時，產生對住宅擔保品估價之需求，以為債權之確保。然銀行的估價過程與一般估價最大不同，肇因於其估價前，擔保品本身已先產生一組買賣契約價格。過去研究指出，估價會嘗試以某些較易取得的價格資訊作為定錨點(anchor)，藉以調整並成為最後的價格。而我國不動產交易價格資訊不透明，契約價格往往由借款人提供的情況下，銀行內部估價人員可能因資訊不易取得、定錨效果，在擔保品的鑑估結果上受到契約價格影響，倘有心人士欲藉此獲得高額貸款、牟取不法利益，將損及銀行債權，即使採用自動估價系統降低人為影響因素，因資料來源不佳，只會產生所謂「garbage in garbage out」的結果。據此，如何分辨契約價格是否具有參考力變成為關鍵，亦為本文欲補足的研究缺口。 本文採用國內某銀行臺北市不動產擔保品8,348筆估價資料為樣本，建立以挑選契約價格是否具有參考力的機率預測模型，尋求影響能判定契約價格是否具有參考力的主要因素，並研究在最適的機率界限下，篩選出具有參考力的契約價格樣本。而研究結果所建立的模型，其預測並篩選出的契約價格樣本均較未經模型篩選者，對擔保品價格之估計有顯著提升。因此本研究所建立的契約價格篩選模型確能提升銀行估價準確性，使不動產擔保品鑑估價格的形成過程中，獲得更多可靠的參考資訊，降低人為操縱的空間，並在成交價格資訊不足的情況下，提升估價人員對契約價格的辨識能力。 / In the face of the borrower to apply for a mortgage of real estate, financial institutions have estimated the price of the collateral requirements to protect the debt claim. However, the biggest difference with the general valuation and that of financial institutions, valuation of its causes before the collateral itself has produced a first sale contract price. In the past research that one attempts to estimate the price of some greater access to information act to anchor in order to adjust and become the final price. Because financial institutions are not easy to obtain price information on real estate transactions in Taiwan, price information is often provided by the borrower. A small number of loans borrower deliberate fraud to forgery or false irrigation Contract price sale and purchase agreement in order to obtain high credit. Even with the automatic valuation system to reduce the human impact factor, due to poor data sources, it will only produce so-called "garbage in garbage out" of the results. Accordingly, how to tell whether the contract price to a reference force becomes critical, and also in this article want to complement the research gap. We adopt 8,348 estate collateral valuation data in Taipei City of a domestic bank for the sample to establish a binary logistic regression model. And we try to seek the main factors that determine whether the contract price of the reference force, and find out the optimal cutoff point, filter out of a sample of the contract price of the reference force. The results confirm the model in this paper. The selected samples of the contract price is estimated that the price of collateral significantly improved compared with those without filtering. Therefore, the model established in this study can really improve the accuracy of bank valuation. Enhance the recognition ability of the bank's internal appraisers on the contract price in the lack of transaction price information.

Air pollution is ranked by the World Health Organisation as one of the top ten contributors to the global burden of disease and injury. Epidemiological studies have shown that exposure to air pollution is associated with cardiorespiratory diseases. However, most of the previous studies have looked at this issue using air pollution data from a single monitoring site or average values from a few monitoring sites in a city. There is increasing concern that the relationships between air pollution and mortality may vary with geographical area, particularly for a big city. This thesis consisted of three interlinked studies that aimed to examine the spatial variation in the relationship between long-term exposure to air pollution and cardiorespiratory mortality in Brisbane, Australia. The first study evaluated the long-term air pollution trends in Brisbane, Australia. Air pollution data used in this study were provided by the Queensland Environmental Protection Agency (QEPA). The data comprised the daily average concentrations of particulate matter less then 10 µm in aerodynamic diameter (PM10), nitrogen dioxide (NO2), ozone (O3) and sulphur dioxide (SO2) between 1 January 1980 and 31 December 2004 in two monitoring sites (i.e. Eagle farm and Rocklea), and in other available monitoring sites between 1 January 1996 and 31 December 2004. Computerised data files of daily mortality between 1 January 1996 and 31 December 2004 in Brisbane city were provided by the Office of Economic and Statistical Research of the Queensland Treasury. Population data and the Socio-Economic Indexes for Areas (SEIFA) data in 2001 were obtained from the Australian Bureau of Statistics (ABS) for each statistical local area (SLA) of the Brisbane city. The long-term air pollution (the daily maximum 1-hour average or daily 24-hour average concentrations of NO2, O3 and PM10) trends were evaluated using a polynomial regression model in two monitoring sites (Eagle Farm and Rocklea) in Brisbane, Australia, between 1980 and 2003. The study found that there were significant up-and-down features for air pollution concentrations in both monitoring sites in Brisbane. Rocklea recorded a substantially higher number of days with concentrations above the relevant daily maximum 1-hour or 24-hour standards than that in Eagle Farm. Additionally, there was a significant spatial variation in air pollution concentrations between these areas. Therefore, the results indicated a need to examine the spatial variation in the relationship between long-term exposure to air pollution and cardiorespiratory mortality in Brisbane. The second study examined the spatial variation of SO2 concentrations and cardiorespiratory mortality in Brisbane between 1999 and 2001. Air pollutant concentrations were estimated using geographical information systems (GIS) techniques at a SLA level. Spatial distribution analysis and a multivariable logistic regression model were employed to investigate the impact of gaseous air pollution on cardiorespiratory mortality after adjusting for potential confounding effects of age, sex, calendar year and SEIFA. The results of this study indicate that for every 1 ppb increase in annual average SO2 concentration, there was an estimated increase of 4.4 % (95 % confidence interval (CI): 1.4 - 7.6 %) and 4.8 % (95 % CI: 2.0 - 7.7 %) in cardiovascular and cardiorespiratory mortality, respectively. We estimated that the excess number of cardiorespiratory deaths attributable to SO2 was 312 (3.4% of total cardiorespiratory deaths) in Brisbane during the study period. Our results suggest that long-term exposure to SO2, even at low levels, is a significant hazard to population health. The final study examined the association of long-term exposure to gaseous air pollution (including NO2, O3 and SO2) with cardiorespiratory mortality in Brisbane, Australia, 1996 - 2004. The pollutant concentrations were estimated using GIS techniques at a SLA level. Logistic regression was used to investigate the impact of NO2, O3 and SO2 on cardiorespiratory mortality after adjusting for potential confounding effects of age, sex, calendar year and SEIFA. The study found that there was an estimated 3.1% (95% CI: 0.4 - 5.8%) and 0.5% (95% CI: -0.03 - 1.3 %) increase in cardiorespiratory mortality for 1 ppb increment in annual average concentration of SO2 and O3, respectively. However there was no significant relationship between NO2 and cardiorespiratory mortality observed in the multiple gaseous pollutants model. The results also indicated that long-term exposure to gaseous air pollutants in Brisbane, even at the levels lower than most cities in the world (especially SO2), were associated with cardiorespiratory mortality. Therefore, spatial patterns of gaseous air pollutants and their impact on health outcomes need to be assessed for an evaluation of long-term effects of air pollution on population health in metropolitan areas. This study examined the relationship between air pollution and health outcomes. GIS and relevant mapping technologies were used to display the spatial patterns of air pollution and cardiorespiratory mortality at a SLA level. The results of this study show that long-term exposure to gaseous air pollution was associated with cardiorespiratory mortality in Brisbane and this association appeared to vary with geographic area. These findings may have important public health implications in the control and prevention of air pollution-related health effects, since now many countries and governments have paid more attention to control wide spread air pollution and to protect our environment and human health.

Obstructive sleep apnoea hypopnoea syndrome (OSAHS) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The reference standard of clinical diagnosis, called Polysomnography (PSG), requires a full-night hospital stay connected to over 15 measuring channels requiring physical contact with sensors. The vast quantity of physiological data acquired during the PSG has to be manually scored by a qualified technologist to assess the presence or absence of the decease. The PSG is inconvenient, time consuming, expensive and unsuited for community screening. The limited PSG facilities around the world have resulted in long waiting lists and a large fraction of patients remain undiagnosed at present. There has been a flurry of recent activities in developing a portable technology to resolve this need. All the devices have at least one sensor that requires physical contact with the subject. Unattended systems have not led to sufficiently high sensitivity/specificity levels to be used in a routine home monitoring or a community screening exercise. OSAHS is a sleep respiratory disorder principally caused by functional deficiencies occurring in the upper airways during sleep. These conditions and the reduced muscle tone during sleep, cause the muscles in the upper airways to collapse partially or completely thus resulting in episodes of hypopnoea and apnoea respectively. During the process leading to collapse of upper airways, upper airways act as an acoustic filter frequently producing snoring sounds. The process of snore sound production leads us to hypothesise that snore sounds should contain information on changes occurring in the upper airways during the OSAHS. Snoring almost always accompanies the OSAHS and is universally recognised as its earliest symptom. At present, however, the quantitative analysis of snore sounds is not a practice in clinical OSAHS detection. The vast potential of snoring in the diagnosis/screening of the OSAHS remains unused. Snoring-based technology opens up opportunities for building community-screening devices that do not depend on contact instrumentation. In this thesis, we present our work towards developing a snore–based non-contact instrumentation for the diagnosis/screening of the OSAHS. The primary task in the analysis of Snore Related Sounds (SRS) would be to segment the SRS data as accurately as possible into three main classes, snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. A new algorithm was developed, based on pattern recognition for the SRS segmentation. Four features derived from the SRS were considered to classify samples of the SRS into three classes. We also investigated the performance of the algorithm with three commonly-used noise reduction (NR) techniques in speech processing, Amplitude Spectral Subtraction (ASS), Power Spectral Subtraction (PSS) and Short Time Spectral Amplitude (STSA) Estimation. It was found that the noise reduction, together with a proper choice of features, could improve the classification accuracy to 96.78%. A novel model for the SRS was proposed for the response of a mixed-phase system (total airways response, TAR) to a source excitation at the input. The TAR/source model is similar to the vocal tract/source model in speech synthesis and is capable of capturing the acoustical changes brought about by the collapsing upper airways in the OSAHS. An algorithm was developed, based on the higher-order-spectra (HOS) to jointly estimate the source and the TAR, preserving the true phase characteristics of the latter. Working on a clinical database of signals, we show that the TAR is indeed a mixed phased signal and second-order statistics cannot fully characterise it. Nocturnal speech sounds can corrupt snore recordings and pose a challenge to the snore-based OSAHS diagnosis. The TAR could be shown to detect speech segments embedded in snores and derive features to diagnose the OSAHS. Finally presented is a novel technique for diagnosing the OSAHS, based solely on multi-parametric snore sound analysis. The method comprises a logistic regression model fed with a range of snore parameters derived from its features — the pitch and Total Airways Response (TAR) estimated using a Higher Order Statistics (HOS) based algorithm. The model was developed and its performance validated on a clinical database consisting of overnight snoring sounds simultaneously recorded during a hospital PSG using a high fidelity sound recording setup. The K-fold cross validation technique was used for validating the model. The validation process achieved an 89.3% sensitivity with 92.3% specificity (the area under the Receiver Operating Characteristic (ROC) curve was 0.96) in classifying the data sets into the two groups, the OSAHS (AHI >10) and the non-OSAHS. These results are superior to the existing results and unequivocally illustrate the feasibility of developing a snore-based non-contact OSAHS screening device.

Obstructive sleep apnoea hypopnoea syndrome (OSAHS) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The reference standard of clinical diagnosis, called Polysomnography (PSG), requires a full-night hospital stay connected to over 15 measuring channels requiring physical contact with sensors. The vast quantity of physiological data acquired during the PSG has to be manually scored by a qualified technologist to assess the presence or absence of the decease. The PSG is inconvenient, time consuming, expensive and unsuited for community screening. The limited PSG facilities around the world have resulted in long waiting lists and a large fraction of patients remain undiagnosed at present. There has been a flurry of recent activities in developing a portable technology to resolve this need. All the devices have at least one sensor that requires physical contact with the subject. Unattended systems have not led to sufficiently high sensitivity/specificity levels to be used in a routine home monitoring or a community screening exercise. OSAHS is a sleep respiratory disorder principally caused by functional deficiencies occurring in the upper airways during sleep. These conditions and the reduced muscle tone during sleep, cause the muscles in the upper airways to collapse partially or completely thus resulting in episodes of hypopnoea and apnoea respectively. During the process leading to collapse of upper airways, upper airways act as an acoustic filter frequently producing snoring sounds. The process of snore sound production leads us to hypothesise that snore sounds should contain information on changes occurring in the upper airways during the OSAHS. Snoring almost always accompanies the OSAHS and is universally recognised as its earliest symptom. At present, however, the quantitative analysis of snore sounds is not a practice in clinical OSAHS detection. The vast potential of snoring in the diagnosis/screening of the OSAHS remains unused. Snoring-based technology opens up opportunities for building community-screening devices that do not depend on contact instrumentation. In this thesis, we present our work towards developing a snore–based non-contact instrumentation for the diagnosis/screening of the OSAHS. The primary task in the analysis of Snore Related Sounds (SRS) would be to segment the SRS data as accurately as possible into three main classes, snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. A new algorithm was developed, based on pattern recognition for the SRS segmentation. Four features derived from the SRS were considered to classify samples of the SRS into three classes. We also investigated the performance of the algorithm with three commonly-used noise reduction (NR) techniques in speech processing, Amplitude Spectral Subtraction (ASS), Power Spectral Subtraction (PSS) and Short Time Spectral Amplitude (STSA) Estimation. It was found that the noise reduction, together with a proper choice of features, could improve the classification accuracy to 96.78%. A novel model for the SRS was proposed for the response of a mixed-phase system (total airways response, TAR) to a source excitation at the input. The TAR/source model is similar to the vocal tract/source model in speech synthesis and is capable of capturing the acoustical changes brought about by the collapsing upper airways in the OSAHS. An algorithm was developed, based on the higher-order-spectra (HOS) to jointly estimate the source and the TAR, preserving the true phase characteristics of the latter. Working on a clinical database of signals, we show that the TAR is indeed a mixed phased signal and second-order statistics cannot fully characterise it. Nocturnal speech sounds can corrupt snore recordings and pose a challenge to the snore-based OSAHS diagnosis. The TAR could be shown to detect speech segments embedded in snores and derive features to diagnose the OSAHS. Finally presented is a novel technique for diagnosing the OSAHS, based solely on multi-parametric snore sound analysis. The method comprises a logistic regression model fed with a range of snore parameters derived from its features — the pitch and Total Airways Response (TAR) estimated using a Higher Order Statistics (HOS) based algorithm. The model was developed and its performance validated on a clinical database consisting of overnight snoring sounds simultaneously recorded during a hospital PSG using a high fidelity sound recording setup. The K-fold cross validation technique was used for validating the model. The validation process achieved an 89.3% sensitivity with 92.3% specificity (the area under the Receiver Operating Characteristic (ROC) curve was 0.96) in classifying the data sets into the two groups, the OSAHS (AHI >10) and the non-OSAHS. These results are superior to the existing results and unequivocally illustrate the feasibility of developing a snore-based non-contact OSAHS screening device.

Readjusting Historical Credit Ratings using Ordered Logistic Re-gression and Principal Component Analysis The introduction of the Basel II Accord as a regulatory document for creditrisk presented new concepts of credit risk management and credit risk mea-surements, such as enabling international banks to use internal estimates ofprobability of default (PD), exposure at default (EAD) and loss given default(LGD). These three measurements is the foundation of the regulatory capitalcalculations and are all in turn based on the bank’s internal credit ratings. Ithas hence been of increasing importance to build sound credit rating modelsthat possess the capability to provide accurate measurements of the credit riskof borrowers. These statistical models are usually based on empirical data andthe goodness-of-fit of the model is mainly depending on the quality and sta-tistical significance of the data. Therefore, one of the most important aspectsof credit rating modeling is to have a sufficient number of observations to bestatistically reliable, making the success of a rating model heavily dependenton the data collection and development state.The main purpose of this project is to, in a simple but efficient way, createa longer time series of homogeneous data by readjusting the historical creditrating data of one of Svenska Handelsbanken AB’s credit portfolios. Thisreadjustment is done by developing ordered logistic regression models thatare using independent variables consisting of macro economic data in separateways. One model uses macro economic variables compiled into principal com-ponents, generated through a Principal Component Analysis while all othermodels uses the same macro economic variables separately in different com-binations. The models will be tested to evaluate their ability to readjust theportfolio as well as their predictive capabilities. / Justering av historiska kreditbetyg med hjälp av ordinal logistiskregression och principialkomponentsanalys När Basel II implementerades introducerades även nya riktlinjer för finan-siella instituts riskhantering och beräkning av kreditrisk, så som möjlighetenför banker att använda interna beräkningar av Probability of Default (PD),Exposure at Default (EAD) och Loss Given Default (LGD), som tillsammansgrundar sig i varje låntagares sannoliket för fallissemang. Dessa tre mått ut-gör grunden för beräkningen av de kapitaltäckningskrav som banker förväntasuppfylla och baseras i sin tur på bankernas interna kreditratingsystem. Detär därmed av stor vikt för banker att bygga stabila kreditratingmodeller medkapacitet att generera pålitliga beräkningar av motparternas kreditrisk. Dessamodeller är vanligtvis baserade på empirisk data och modellens goodness-of-fit,eller passning till datat, beror till stor del på kvalitén och den statistiska sig-nifikansen hos det data som står till förfogande. Därför är en av de viktigasteaspekterna för kreditratingsmodeller att ha tillräckligt många observationeratt träna modellen på, vilket gör modellens utvecklingsskede samt mängdendata avgörande för modellens framgång.Huvudsyftet med detta projekt är att, på ett enkelt och effektivt sätt, skapaen längre, homogen tidsserie genom att justera historisk kreditratingdata i enportfölj med företagslån tillhandahållen av Svenska Handelsbanken AB. Jus-teringen görs genom att utveckla olika ordinala logistiska regressionsmodellermed beroende variabler bestående av makroekonomiska variabler, på olikasätt. En av modellerna använder makroekonomiska variabler i form av princi-palkomponenter skapade med hjälp av en principialkomponentsanalys, medande andra modelelrna använder de makroekonomiska variablerna enskilt i olikakombinationer. Modellerna testas för att utvärdera både deras förmåga attjustera portföljens historiska kreditratings samt för att göra prediktioner.

Scholars have proposed the idea that trust influences individuals’ choice to vote or abstain. However, there is uncertainty about the composition of trust and its effect on voter turnout. The aim of this study is to explore the relationship between interpersonal and institutional trust and voter turnout in South Africa. Examining presently unused data for South Africa from the World Values Survey 2006 through exploratory and confirmatory factor analysis, the argument is advanced that trust is a multidimensional concept that may be modelled by multivariate measurements. A logistic factor score regression model shows that a one-unit increase of trust in public institutions on average increases the odds of voting by 9 % whereas trust in private institutions and interpersonal trust have no significant effects. The results imply that trust- strengthening actions may be of interest to South African public institutions to increase electoral participation and legitimise election outcomes.