Tesis sobre el tema "Quality scoring"

Cette thèse examine le problème de l’évaluation de la qualité d’information produite par des capteurs : ces derniers ne fournissent pas toujours une information correcte et l’évaluation de sa qualité est nécessaire pour en permettre une utilisation optimale. Nous proposons une approche qui ne fait pas d’hypothèse sur le type de capteur considéré, ne requiert pas de vérité-terrain et dépend seulement de méta-informations aisées à obtenir, qui exploitent des attributs partagés par la plupart des capteurs. Le modèle proposé est appelé ReCLiC, du nom en anglais des quatre dimensions sur lesquelles il repose : fiabilité, compétence, vraisemblance et crédibilité (Reliability, Competence, Likelihood et Credibility). Nous discutons les contraintes et propriétés souhaitées de ces dimensions et proposons des définitions motivées pour chacune d’entre elles. De plus, nous proposons une implémentation de la définition générique de ReCLiC pour un problème réel, dans le domaine de la signalisation ferroviaire : nous discutons de leur forme pour cette application et effectuons une analyse théorique du comportement du modèle de cotation d’information auquel elles conduisent, en examinant chaque dimension séparément. Cette implémentation est validée expérimentalement en utilisant des données simulées réalistes, générées selon un protocole expérimental permettant de contrôler les problèmes de qualité introduits. Le modèle ReCLIC est de plus mis en oeuvre sur des données ferroviaires réelles, en utilisant une méthode de visualisation originale qui permet en outre d'étudier la notion de dynamique de la confiance
In this thesis, the problem of assessing information quality produced by sensors is investigated. Indeed, sensors, usually used in networks, do not always provide correct information and the scoring of this information is needed. An approach is proposed that deals with some of the major limitations in the literature by providing a model designed to be sensor-generic, not dependent on ground truth and dependent only on easy-to-access meta-information, exploiting only attributes shared among the majority of sensors. The proposed model is called ReCLiC from the four dimensions that it considers: Reliability, Competence, Likelihood and Credibility. The thesis discusses in depth the requirements of these dimensions and proposes motivated definitions for each of them. Furthermore, it proposes an implementation of the generic ReCLiC definition to a real case, for a specific sensor in the railway signalling domain: the form of the four dimensions for this case is discussed and a formal and experimental study of the information scoring behaviour is performed, analysing each dimension separately. The proposed implementation of the ReCLiC model is experimentally validated using realistic simulated data, based on an experimental protocol that allows to control various quality issues as well as their quantity, Finally, the ReCLiC model is used to analyse a real datasetapplying a new visualisation method that, in addition, allows to study the notion of trust dynamic

The hypothesis of this work is that through establishing the background variation of ultrasonographic fetal biometry measurements and elucidating the parameters that influence these measurements, a new Fetal Ultrasound Biometry Quality (FUB-Q) image-scoring tool can be created which will be reproducible and able to quantify the accuracy of fetal measurements. Six studies are included, each answering a specific research question. The aim of the first study was to ascertain whether pre-existing image quality scoring methods reflect measurement accuracy and reproducibility. It demonstrated that during the course of an exercise where there was demonstrable improvement in the consistency of measurements performed - by a group of sonographers, this was not mirrored by the pre-existing image scoring system. The aim of the second study was to establish the intra- and inter- observer variability of fetal biometry measurements throughout pregnancy by expert sonographers. This study demonstrated that ultrasound variability of fetal biometry increases with advancing gestation when expressed in measurement values, but is constant as a percentage of the fetal dimensions or when reported as a z score. Calliper placement was the major component of the overall variability. The values from this study served as the background variability, "reference standard", for the FUB-Q tool. The third study had two aims. The first was to establish how 3D scanning performs compared to conventional, real-time, 20. The second aim was to assess whether off-line 3D volume manipulation can be used as a tool to substitute real-time 20 ultrasound for the subsequent studies. It demonstrated that measurements using 3D volume acquisitions exhibit good agreement with real-time 20 scanning, with no systematic error but with a higher random error. However, it also demonstrated that 3D scanning is slower to perform and, similar to real-time 2D, it is not always possible to acquire a 3D volume from a desired orientation. Furthermore, not all 3D volume acquisitions were amenable to reconstruction. However, this study showed that saved 3D volumes can be used as a mean to store large volumes of data for later detailed analysis. The aim of the fourth study was to create the FUB-Q scoring tool. This was done by establishing the difference in measurement resulting from optimal and different forms of suboptimal images in a systematic fashion. For any . t " given image, and its derived measurement, the observer inserts in the model the various image scoring point parameters. The model then gives a prediction about the confidence interval within which the optimal, "gold standard", measurement should be. The aim of the fifth study was to validate on an independent test set the predictive ability of the newly developed FUB-Q scoring tool. It demonstrated that the FUB-Q tool can correctly predict the confidence interval within which measurements recorded from correctly acquired images should be in relation to measurements acquired from incorrectly acquired ones. The aim of the sixth, and final , study was to evaluate the reproducibility of obtaining the relevant scores for the FUB-Q tool. It demonstrated that the FUB-Q tool has good intra- and inter- observer reproducibility and is a reliable system for assessing the quality of fetal biometry based on ultrasound images. In conclusion, the FUB-Q tool could be a useful system used for audit of clinical practice and quality control as well as for training purposes .

Bibliography: pages 139-150.
The South African Scoring System (SASS) is a rapid bioassessment technique based on one component of riverine biotas, the benthic macroinvertebrates. Each taxon is assigned a tolerance/sensitivity score, which are summed to provide a Total Score. The Average Score per Taxon (ASPT) is calculated by dividing this Total Score by the number of taxa. This study was undertaken to evaluate the SASS technique as a tool for the assessment of water quality. Three study-sites, which differed in water quality, in the south-western Cape were selected for a detailed investigation into sample variability and replication of two methods of biological assessment, namely quantitative box-sampling and SASS. The ability of each method to differentiate between these sites was determined. The more general application of SASS in the south-western Cape was examined at forty nine sites and potential problems associated with SASS, namely biotope availability, temporal variability and longitudinal changes were investigated. A minimum of twelve and four quantitative samples is needed to ensure collection of 95% or 75% of benthic macroinvertebrate taxa respectively. Sampling within a single biotope component, such as a "riffle" or "run" would reduce the number of samples needed. A minimum of four and two SASS samples is needed to ensure collection of 95% or 75% of the taxa respectively. This technique is however designed such that only one sample is taken per site. The Total Score that one sample would produce as a percentage of the Total Score from 20 samples, were 28% , 59% and 45% for Sites 1, 2 and 3 respectively. Total Score increases with increasing sampling effort, whilst ASPT is relatively unaffected by sampling effort. ASPT should therefore be used in interpretation of scores. Variability, as determined by both quantitative sampling and rapid bioassessment, was greatest at the least impacted site. Such sites should be more intensively sampled, either by increasing the number of box- samples taken, or by increasing the time period for SASS sampling.

This study was an attempt to add to existing research by estimating the ability of third-party examiners to assess whether or not an organization successfully implemented strategies based on the criteria of the Texas Award for Performance Excellence (TAPE). The TAPE is given each year by the Quality Texas Foundation and recognizes organizations that demonstrate superior performance as it is defined by customer satisfaction and continuous improvement. The TAPE is a state-level award for quality that uses the same criteria as the Malcolm Baldrige National Quality Award for Performance Excellence. This research was an analysis of the TAPE process at the level of examiners, also known as the Board of Examiners. The Board is made up of approximately 150 experienced professionals from several types of business sectors and is responsible for evaluating organizational self-assessments. In this quantitative study, data were converted from the Quality Texas Foundation into a database. Because the set of the TAPE applicants included in the study consisted of the entire population of TAPE applicants selected from 2001 to 2004, descriptive statistics were appropriate for producing informative data that could be analyzed for variation and stability in the scoring process. Exploration of patterns in descriptive statistics and multivariate analysis of variance were the primary tools used in this particular study along with Cronbach’s Alpha as an indicator of reliability. Since scoring for the TAPE is based on an individual examiner’s best subjective assessment, it was impossible to have one objective score against which all the other scores could be measured. The team consensus score was therefore used as the true score for measurement. Establishing reliability of examiners’ scores was a problem due to the fact that organizations and teams did not repeat. Results from the study led to the conclusion that there was insufficient evidence to make a determination on what influences examiners’ scoring consistency. More data will need to be collected in such a way so as to make it possible to identify that impact consistency of examiner scores.

This paper deals with a measure theoretical model for evaluation of e-learning programs. Based on methods of general measure theory an evaluation model is developed which can be used for assessment of complex target structures in context of e-learning programs. With the presented rating function target structures can be evaluated by a scoring value which indicates how the targets in sense of a given logical target structure has been reached. A procedure is developed for the estimation of scoring values for target structures based on adapted assessment checklists.

The theoretical part of thesis covers consumer credit, particular parts of credit process and credit registers. Analysis of credit risk management in a bank of concern, quality of credit portfolio and suggestion of changes in rating of retail receivables are dealt with in the practical part.

>Magister Scientiae - MSc
The water quality in many Cape Town Rivers and streams is a major challenge. Kuils River is subject to multiple land use impacts from upstream to downstream because of rapid urbanization in its catchment area. The main pollution sources are urban and industrial, organic matter from litter under the road-bridge, and golf course. However no systematic efforts have been made to evaluate and improve the health of the river in term of management. To assess impacts on water quality, this study was conducted from 4th September to 27th November 2012 in 5 selected sites in the upper reach of the Kuils river. The main aim was to compare the health of the river in 2012 with that found in 2005 using physical and chemical characteristics and the South Africa Scoring System (SASS). The statistical analysis showed a significant difference between and within sites. The water temperature, pH, dissolved oxygen concentration, total dissolved solids (TDS), and salinity were collected in situ by YSI 30 meter. To evaluate nutrient (nitrate and phosphorus) concentrations water samples were analyzed at UWC laboratory using spectrophotometer. In addition human activities, basic conditions (7.13 to 8.76), high total dissolved solids (416 to to 916.5 mg L¯¹) and salinity (0.31 to 0.71 mg L¯¹) concentrations were influenced by Malmesbury shales. Nitrate (0.1 to 3.1 mg L¯¹) and phosphorus (0.11 to 5.27 mg L¯¹) concentrations and the decrease in dissolved oxygen in November 2012 showed eutrophic conditions of the river. In the tributary site phosphorus (1.32 to 3.62 mg L¯¹) concentrations revealed hypertrophic condition compared to South Africa guideline. Macroinvertebrates sampled showed a total of 28 taxa grouped in 11 orders were sampled. Poor habitat diversity and water quality degradation were principal causes of low species diversity. The South Africa Score System version 5 (SASS5) and Average Score per Taxon (ASPT) indicated that the river is seriously impacted in 2012 compared to 2005 where water quality was in poor condition. The SASS and the ASPT scores were less than 50 and 4.2 at all sampling sites in most part of sampling period.

Pinhole leaks in copper plumbing pipes are a big concern for the homeowners. The problem is spread across the nation and remains a threat to plumbing systems of all ages. Due to the absence of a single acceptable mechanistic theory no preventive measure is available to date. Most of the present mechanistic theories are based on analysis of failed pipe samples however an objective comparison with other pipes that did not fail is seldom made. The variability in hydraulic and water quality parameters has made the problem complex and unquantifiable in terms of plumbing susceptibility to pinhole leaks. The present work determines the spatial and temporal spread of pinhole leaks across United States. The hotspot communities are identified based on repair histories and surveys. An assessment of variability in water quality is presented based on nationwide water quality data. A synthesis of causal factors is presented and a scoring system for copper pitting is developed using goal programming. A probabilistic model is presented to evaluate optimal replacement time for plumbing systems. Methodologies for mechanistic modeling based on corrosion thermodynamics and kinetics are presented.
Master of Science

Research in the area of public procurement has gained increasing attention with increasing interest in researching different evaluation methods for incoming tenders in procurement. The study analyzed the structure for procurement with a multi-criteria model for observation of changes in ranking and studied the importance of correct scoring. A sensitivity and robustness analysis were used as a tool to study the stability and the strength of a contracting entity's priorities. The purpose of this thesis was a study to determine if the ranking of tenders became more sensitive to the structure of the model or for each tender's evaluation, where it was found that the ability to correctly score points was more crucial than being critical of weights assigned to the criteria in the procurement, respectively. Relationship between price and quality turned out to be unknown to some extent, the connection could be determined after all tenders were submitted and accumulated points were available.
Forskning inom området offentlig upphandling har fått allt mer uppmärksamhet när intresset hamnat på undersökning av olika utvärderingsmetoder för inkommande anbud vid en upphandling. I studien analyserades uppbyggnaden för upphandlingar med en multikriteriemodell för observation över förändringar hos rangordning samt studerandet av vikten för en korrekt poängsättning. En känslighets- och robusthetsanalys tillämpades som verktyg för att studera stabiliteten och styrkan hos en upphandlande entitets prioriteringar. Syftet med uppsatsen var en undersökning för en bedömning om rangordningen av upphandlingars anbud blev känsligast mot strukturen på modellen eller för varje anbuds värdering, där det visade sig att förmågan för korrekt poängtilldelning var mer avgörande än att vara kritisk till vikterna som tilldelades respektive kriterier inom upphandlingen. Relationen mellan pris och kvalitet visade sig vara okänd till viss del, sambandet kunde avgöras efter att alla anbud var inskickade och samlade poäng fanns att tillhandahålla.

Роботу виконано в Інституті прикладного системного аналізу Національного технічного університету України «Київський політехнічний інститут імені Ігоря Сікорського».
У дисертаційній роботі розроблено системну методологію аналізу та оцінювання фінансових ризиків, яка ґрунтується на принципах системного аналізу та менеджменту ризиків, а також запропонованих принципах адаптивного та динамічного менеджменту ризиків. Методологія включає: комбінований метод обробки неповних та втрачених даних, ймовірнісно-статистичний метод оцінювання ризику фінансових втрат, динамічний метод оцінювання ризиків, який передбачає побудову різних типів моделей виживання, метод структурно-параметричної адаптації, застосування скорингової карти до аналізу ризиків фінансових систем і нейро-нечіткий метод доповнення вибірки відхиленими заявками. Містить критерії урахування інформаційного ризику, оцінки якості даних, прогнозів та рішень, квадратичний критерій якості опрацювання ризику та інтегральну характеристику оцінювання ефективності методів менеджменту ризиків. Практична цінність одержаних результатів полягає у створенні розширеної інформаційної технології та інформаційної системи підтримки прийняття рішень на основі запропонованої системної методології.

碩士
國立彰化師範大學
企業管理學系國際企業經營管理
97
Abstract Using the database of a branch of C Bank in central Taiwan on its loans to small and medium-size enterprises up till December 2008,( 120 normal loans, overdue loans 32, for a total of 152) this study discusses how credit quality was affected by the rating mechanisms (including the financial structure, the solvency, the profitability, the operating performance and so on) applied on such entities. The approach of analysis of variance was first used to evaluate variables of the samples. A variable would be deemed to have effect on credit quality if its average number deviates too much. The Chi-square test was then utilized to determine whether the results are changed with the independent variables. Finally, the Logistic Regression Model was employed as a comprehensive means to verify those critical variables posing influence on credit quality. The purpose is to set up a more effective method for risk management so as to provide crucial reference to the credit operations of banks. Keywords: Credit Rating、 Loans、 Overdue Loans、 Credit Quality

博士
國立交通大學
經營管理研究所
105
A credit scoring model integrates a statistical method with the opinions of an expert group and therefore contains a combination of risk factors and weights. The assignment of weights in the modeling processes is a central interest of and a prime challenge for most financial institutions. Traditionally, an expert group determines the weights in accordance with the experts’ views on the importance of the various risk factors. However, the group’s members often have conflicting objectives (i.e., risk minimization vs. market share maximization) and can be dominated by a single dominating opinion maker, which leads to bias and poor model performance. The analytic hierarchy process (AHP) is a multiple-criteria decision-making tool that has been successfully used in various fields. This study is the pioneer to apply the AHP method to the credit scoring process to create a model that increases the predictive power. The study then tests the AHP model in a case study and compares the results with existing scoring techniques. The findings suggest that the AHP scoring model significantly improves the credit scoring model’s predictive ability.

碩士
元智大學
管理研究所
96
As the opening of the banking industry happens during these years, many banks start to compete in the SEM business. However, SME financial information inconsistency and short business life cycle are typical problems faced by the banks. As a result, risk control has become am important issue for SME credit business. SME credit scoring, the threshold for SME credit business is studied in this research. Understanding the relationship between scoring variants and the credit quality helps to understand if the scoring table provides useful information for credit approval. Logistic Regression is applied to understand which variant is significant to credit quality. As a result, there are 6 significant variants. Although not every variant is significant in this research, but these variants are still valuable for credit approval references.

碩士
朝陽科技大學
營建工程系碩士班
98
Public construction engineering is one of the significant elements to enhance the national competitiveness. In order to strengthen and improve the engineering quality, the Public Construction Commission had enacted “System of Quality Management for Public Engineering Construction” and began to execute the inspection since 2002, which aimed to make all the parties participating in the actual engineering work, including the principals, construction advisors, and contractors, recognize the importance of engineering quality. This study collected a total of 52 inspection cases of Taichung County Government between 2007 and 2008, and collated the data in the checklist about other flaws which inspectors failed to well indentify or the choices were not precisely defined. After correcting all the mistakes and misplaced errors as well as slightly modifying the contents and words, this study discovered that the number of other flaws for agencies reduced to 1 from 22; the numbers of other flaws for construction supervisors and contractors, which were 26 and 24 respectively, returned zero while they were completely relocated to relevant flaw items; the number of other flaws for construction quality and intensity reduced to 2 from 61; the number of other flaws for intensity and safety, which were 37 and 39 respectively, returned zero while they were completely relocated to relevant flaw items.

碩士
國立彰化師範大學
企業管理學系
107
ABSTRACT This research, using evaluation and assessment provided by appropriate suppliers, ensures the data to become more transparent and closer to reality through the assessment and evaluation process management. Even under the circumstances of having more subjective than objective observations, more information still can be distilled from original data. This study will use a passive component company as a case. In regards to the supplier evaluation method, there are nine sub-evaluation indicators. All of aforementioned indicators add up to 100% and will be assessed and explored both subjectively and objectively. In order to effectively establish a monitoring mechanism for a supplier evaluation and find possible anomalies (i.e. over- or under-estimations), we will use K-Means method to target the evaluations of A and B factories from the first quarter of 2017 to the first quarter of 2018 by comparing the original evaluation result with that of the K-Means method categorized into three probable outcomes (i.e. A, B, and C). Grades A, B, and C that belong to qualified suppliers represent a score of 95 points and more, a score of 90 but below 95 points, and a score of 85 but below 90 points, respectively. We hope to find and address the problem through this study, as well as establish a set of objective evaluations to increase quality and ensure consistent supply in the long run.

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and Econometrics
The modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed situations, financial crises, and pandemics are the source of many new risks with new requirements such as new data sources and different models. This dissertation aims to show the data quality challenges of high-risk situations such as pandemics or economic crisis and it try to theorize the new machine learning models for predictive and longitudes time series models. In the first study (Chapter Two) we analyzed and compared the quality of official datasets available for COVID-19 as a best practice for a recent high-risk situation with dramatic effects on financial stability. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organizations based on the value of systematic measurement errors. We combined excel files, text mining techniques, and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data. In the second study (Chapter Three) we discussed credit risk as the most significant source of risk in banking as one of the most important sectors of financial institutions. We proposed a new machine learning approach for online credit scoring which is enough conservative and robust for unstable and high-risk situations. This Chapter is aimed at the case of credit scoring in risk management and presents a novel method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study its impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which can reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability. In the third study (Chapter Four) we focus on the financial stability challenges faced by insurance companies and pension schemes when managing systematic (undiversifiable) mortality and longevity risk. For this purpose, we first developed a new ensemble learning strategy for panel time-series forecasting and studied its applications to tracking respiratory disease excess mortality during the COVID-19 pandemic. The layered learning approach is a solution related to ensemble learning to address a given predictive task by different predictive models when direct mapping from inputs to outputs is not accurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, the appropriate holdout for each model is specified individually. Additionally, the models in the ensemble are selected by a proposed selection approach to be combined dynamically based on their predictive performance. It provides a high-performance ensemble model to automatically cope with the different kinds of time series for each panel member. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series (countries) of reported respiratory disease deaths with monthly sampling frequency to show the amount of improvement in predictive performance. We then compare each country’s forecasts of respiratory disease deaths generated by our model with the corresponding COVID-19 deaths in 2020. The results of this large set of experiments show that the accuracy of the ensemble model is improved noticeably by using different holdouts for different contributed time series methods based on the proposed model selection method. These improved time series models provide us proper forecasting of respiratory disease deaths for each country, exhibiting high correlation (0.94) with Covid-19 deaths in 2020. In the fourth study (Chapter Five) we used the new ensemble learning approach for time series modeling, discussed in the previous Chapter, accompany by K-means clustering for forecasting life tables in COVID-19 times. Stochastic mortality modeling plays a critical role in public pension design, population and public health projections, and in the design, pricing, and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast the mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this Chapter, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends, and use ensemble learning to forecast life expectancy and annuity prices by age and sex. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19. In this dissertation’s conclusions (Chapter Six), we provide more detailed insights about the overall contributions of this dissertation on the financial stability and risk management by data science, opportunities, limitations, and avenues for future research about the application of data science in finance and economy.