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This thesis stems from my research into the broad area of (credit) scoring and the predicting, understanding and explaining of consumer behaviour. This research started at the Univers1ty of Edinburgh on an ESRC funded project in 1988. This work, which is being submitted as the partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough Unvers1ty, consists of an introductory chapter and a selection of papers published 1991 - 2001 (inclusive). The papers address some of the key issues and areas of interest and concern arising from the rapidly evolving and expanding credit (card) market and the highly competitive nature of the credit industry. These features were particularly evident during the late 1980's and throughout the 90's Chapter One provides a general background to the research and outlines some of the key (practical) issues involved in building a (credit) scorecard Additionally, it provides a brief summary of each of the research papers appearing in full in Chapters 2- 9 (inclusive) and ends with some general limitations and conclusions. The research papers appearing in Chapters 2-9 inclusive) are all concerned with predicting, understanding and explaining different types of consumer behaviour in relation to the use of credit cards. For example discriminating between 'GOOD' and 'BAD' repayers of credit card debt on the basis of different definitions of good and bad, the identification of 'slow payers' using different statistical methods; examining the characteristics of credit card users and non-users, and identifying the characteristics of credit card holders most likely to return their credit card.

Background: To describe the scoring methodology and MRI assessments used to evaluate the cross-sectional features observed in cases and controls, to define change over time for different MRI features, and to report the extent of changes over a 24-month period in the Foundation for National Institutes of Health Osteoarthritis Biomarkers Consortium study nested within the larger Osteoarthritis Initiative (OAI) Study. Methods: We conducted a nested case-control study. Cases (n = 406) were knees having both radiographic and pain progression. Controls (n = 194) were knee osteoarthritis subjects who did not meet the case definition. Groups were matched for Kellgren-Lawrence grade and body mass index. MRIs were acquired using 3 T MRI systems and assessed using the semi-quantitative MOAKS system. MRIs were read at baseline and 24 months for cartilage damage, bone marrow lesions (BML), osteophytes, meniscal damage and extrusion, and Hoffa- and effusion-synovitis. We provide the definition and distribution of change in these biomarkers over time. Results: Seventy-three percent of the cases had subregions with BML worsening (vs. 66 % in controls) (p = 0.102). Little change in osteophytes was seen over 24 months. Twenty-eight percent of cases and 10 % of controls had worsening in meniscal scores in at least one subregion (p < 0.001). Seventy-three percent of cases and 53 % of controls had at least one area with worsening in cartilage surface area (p < 0.001). More cases experienced worsening in Hoffa- and effusion synovitis than controls (17 % vs. 6 % (p < 0.001); 41 % vs. 18 % (p < 0.001), respectively). Conclusions: A wide range of MRI-detected structural pathologies was present in the FNIH cohort. More severe changes, especially for BMLs, cartilage and meniscal damage, were detected primarily among the case group suggesting that early changes in multiple structural domains are associated with radiographic worsening and symptomatic progression.

Zugl.: Dresden, Techn. Universiẗat, Diss., 2007.

After the fifteen years over banking, the financial tsunami reveals that Taiwan banking industry has been in a predicament. In the recent ten years, due to being impacted by the risks of the enterprise finance, the retailer finance, the overseas investment, and even the whole economic system, the banks in Taiwan not only have lost seriously, but also been managed more hardly. How to find out a profit model based on the security, the benefit, and the public welfare principles is the critical issue. The traditional loan to small and medium-sized enterprises that brings the reasonable interest gains and the overall financial intercourse spin-off benefits has become the focal point once again. In order to create the real profit, it is important to control credit risks and the cost of operation. At this time, the government implements the new Basel¢º supervisory standard with the purpose of encouraging the banks to adopt the IRB to estimate the loan credit risks. It has to meet various the lowest operational requirements and statistical analysis patterns as well as should be practiced in the banking loan business definitely. Consequently, to build an internal loan credit scoring system with the scientific method is a key point. The research aims to produce the credit scoring model using a series of logical processes, which derived from the 2,517 small business loan samples from May, 2005 to May, 2006 of one Taiwan commercial bank. It adopted WOE model to evaluate a variety of variables, and sift out the 11 representative and the statistical items. Then, following IRB standard, Logistics Regression and related statistic analysis techniques established the credit estimating method and the linked addition scoring card. Finally, the investigation employed the violation rate distribution, Lorenz¡¦s Curve, K-S Test and Log Odds to make sure the rationality and reliability. Based on this model, there are eight essential variables that affects the verification of the loan to small businesses, including customer present loan situation, the urgent of increasing the loan, repayments custom, and so on, which conform to the banking practical know-how. Therefore, the model could assist the banking employees to calculate the loan credit grades efficiently and further make the accurate judgment.

Konstanz, Univ., Diss., 2008.

Credit scoring is a mechanism used to quantify the risk factors relevant for an obligor’s ability and willingness to pay. Credit scoring has become the norm in modern banking, due to the large number of applications received on a daily basis and the increased regulatory requirements for banks. In this study, the concept and application of credit scoring in a South African banking environment is explained, with reference to the International Bank of Settlement’s regulations and requirements. The steps necessary to develop a credit scoring model is looked at with focus on the credit risk context, but not restricted to it. Applications of the concept for the whole life cycle of a product are mentioned. The statistics behind credit scoring is also explained, with particular emphasis on logistic regression. Linear regression and its assumptions are first shown, to demonstrate why it cannot be used for a credit scoring model. Simple logistic regression is first shown before it is expanded to a multivariate view. Due to the large number of variables available for credit scoring models provided by credit bureaus, techniques for reducing the number of variables included for modeling purposes is shown, with reference to specific credit scoring notions. Stepwise and best subset logistic regression methodologies are also discussed with mention to a study on determining the best significance level for forward stepwise logistic regression. Multinomial and ordinal logistic regression is briefly looked at to illustrate how binary logistic regression can be expanded to model scenarios with more than two possible outcomes, whether on a nominal or ordinal scale. As logistic regression is not the only method used in credit scoring, other methods will also be noted, but not in extensive detail. The study ends with a practical application of logistic regression for a credit scoring model on data from a South African bank. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Mathematics and Applied Mathematics / unrestricted

Objective and accurate techniques are needed to estimate age and antler size of live white-tailed deer (Odocoileus virginianus), because these parameters are essential to many white-tailed deer management strategies. I developed and evaluated accuracy of methods for estimating age and antler size from photographs of live, male white-tailed deer using Geographic Information Systems (GIS). I estimated size of photographed, known-score mounted antlers accurately using a fixed-scale object and photographed, live deer using anatomical features. I determined if a series of morphometric ratios could be used to predict age of deer from photographs using a dichotomous key procedure. Mean percentage error for gross antler score was < 6% using a single photograph at 0° or 45°. The dichotomous key procedure effectively separated age classes of photographed, live white-tailed deer. When grouping deer into 1, 2, 3, 4, or ≥ 5 year age classes, the methodology respectively.

Mathematical programming (MP) can be used for developing classification models for the two–group classification problem. An MP model can be used to generate a discriminant function that separates the observations in a training sample of known group membership into the specified groups optimally in terms of a group separation criterion. The simplest models for MP discriminant analysis are linear programming models in which the group separation measure is generally based on the deviations of misclassified observations from the discriminant function. MP discriminant analysis models have been tested extensively over the last 30 years in developing classifiers for the two–group classification problem. However, in the comparative studies that have included MP models for classifier development, the MP discriminant analysis models either lack appropriate normalisation constraints or they do not use the proper data transformation. In addition, these studies have generally been based on relatively small datasets. This thesis investigates the development of MP discriminant analysis models that incorporate appropriate normalisation constraints and data transformations. These MP models are tested on binary classification problems, with an emphasis on credit scoring problems, particularly application scoring, i.e. a two–group classification problem concerned with distinguishing between good and bad applicants for credit based on information from application forms and other relevant data. The performance of these MP models is compared with the performance of statistical techniques and machine learning methods and it is shown that MP discriminant analysis models can be useful tools for developing classifiers. Another topic covered in this thesis is feature selection. In order to make classification models easier to understand, it is desirable to develop parsimonious classification models with a limited number of features. Features should ideally be selected based on their impact on classification accuracy. Although MP discriminant analysis models can be extended for feature selection based on classification accuracy, there are computational difficulties in applying these models to large datasets. A new MP heuristic for selecting features is suggested based on a feature selection MP discriminant analysis model in which maximisation of classification accuracy is the objective. The results of the heuristic are promising in comparison with other feature selection methods. Classifiers should ideally be developed from datasets with approximately the same number of observations in each class, but in practice classifiers must often be developed from imbalanced datasets. New MP formulations are proposed to overcome the difficulties associated with generating discriminant functions from imbalanced datasets. These formulations are tested using datasets from financial institutions and the performance of the MP-generated classifiers is compared with classifiers generated by other methods. Finally, the ordinal classification problem is considered. MP methods for the ordinal classification problem are outlined and a new MP formulation is tested on a small dataset.

International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California / Testing of airborne weapons systems often requires that a scoring system be placed on the target drone to obtain critical miss distance data. Advanced weapons utilizing directional warheads often require a scoring system which yields vector, miss distance and miss direction, information. Scalar scoring systems currently in use are relatively simple and inexpensive. Vector scoring systems are typically complex and the cost of systems which are currently available or are being developed can be prohibitively expensive. Due to the current military budget decline, development of a low cost vector scoring system is desirable This paper introduces a low cost vector scoring system developed for airborne target drones and based on an inexpensive scalar scoring system currently in use. To meet the low cost criteria, vector operation is achieved via minimal modifications to the existing scalar system.