The purpose of this study was to investigate if early childhood educators could reliably score items using a new scoring system for the Assessment, Evaluation, and Programming System for Infants and Children (AEPS; Bricker, 2002). The participants were university students completing their certification in Interdisciplinary Early Childhood Education (IECE) at the University of Kentucky (UK). The six participants completed training on implementing the AEPS and administered the AEPS to measure child outcomes. The results of this study validated the new scoring system for the AEPS by illustrating that the participants could reliably score a curriculum based assessment.

Hydropathic molecular modeling is a computer-aided molecular design technique for obtaining, representing, and understanding the properties and interactions of biomacromolecular complexes in the biological environment. Hydropathic INTeraction (HINT) is a novel empirical force field to calculate the free energy of intermolecular interaction based on experimentally determined partition coefficients (log Po/w). It includes all the expected interactions between molecules such as hydrogen bonding, hydrophobic, electrostatic, acid-base, and Coulombic interactions, entropy, solvation and others. HINT tools were used to determine, evaluate, and analyze protein-ligand interactions in different research projects: 1) We used these tools to discover small molecule inhibitors of PsaA, a potential target for Streptococcus pneumoniae. We screened and scored potential molecules to obtain hits. After the growth conditions for both the wild type and PsaA mutant of S. pneumoniae were optimized, we then tested our hits. A few compounds passed through the three-stage assay protocol and confirmed the inhibition of PsaA with MICs between 125-250 μM. 2) The SAR of C-3 and C-5 pyrrole-based antitubulin agents at the colchicine-binding site with explicitly solvated models was performed. After docking with GOLD at the colchicine site, post-docking scoring and evaluation were performed with HINT. The total HINT score correlates with binding and activity; similarly, the significance of individual functional groups, protein residues and interactions amongst a collection of compounds can be quantitated. The possibility of water-mediated interactions in a way solvent accessible part of the pocket was considered by subjecting molecular models to MD simulations. Several water molecules were identified to be contributing to the binding and were confirmed by HINT scoring. Finally, using hydropathic molecular modeling tools helped us to understand, evaluate, analyze, and improve protein-ligand interactions in different biological systems.

Cette thèse porte sur le développement d'une méthode non-paramétrique pour l'apprentissage supervisé de règles d'ordonnancement à partir de données étiquetées de façon binaire. Cette méthode repose sur le partitionnement récursif de l'espace des observations et généralise la notion d'arbre de décision au problème de l'ordonnancement, les règles de score produites pouvant être représentées graphiquement par des arbres binaires et orientés. Afin de proposer une méthode d'apprentissage flexible, nous introduisons une procédure permettant, à chaque itération de l'algorithme, de scinder l'espace des observations selon diverses règles, adaptatives et complexes, choisies en fonction du problème considéré. De plus, pour lutter contre le phénomène de sur-apprentissage, nous proposons deux procédures de sélection de modèle, fondées sur la maximisation de l'ASC empirique pénalisée par une mesure de la complexité du modèle. Enfin, dans le but de réduire l'instabilité des arbres d'ordonnancement, inhérente à leur mode de construction, nous adaptons deux procédures d'agrégation de règles de prédiction ré-échantillonnées : le bagging (Breiman, 1996) et les forêts aléatoires (Random Forests, Breiman, 2001). Une étude empirique comparative entre différentes configurations de l'algorithme et quelques méthodes de l'état de l'art est présentée, ainsi que l'application à la problématique industrielle de l'objectivation des prestations d'un véhicule automobile. De plus, nous exploitons cette méthode de scoring pour introduire une heuristique de test d'homogénéité entre deux populations, permettant de généraliser les tests de rangs au cas multi-dimensionnel.

This dissertation consists of four papers, each with an application of a discrete dependent variable model, censored regression or duration model to a credit market phenomenon or monetary policy question. The first three essays deal with bank lending policy, while the last one studies interest rate policy by Central Banks. In the first essay, a bivariate probit model is estimated to contrast the factors that influence banks’ loan granting decision and individuals’ risk of default. This model is used as a tool to construct a Value at Risk measure of the credit risk involved in a portfolio of consumer loans and to investigate the efficiency of bank lending policy. The second essay takes the conclusions from the first paper as a starting point. It investigates if the fact that banks do not minimize default risk can be explained by the existence of return maximization policy. For this purpose, a Tobit model with sample selection effects and variable censoring limits is developed and estimated on the survival times of consumer loans. The third paper focuses on dormancy, instead of default risk or survival time, as the most important factor affecting risk and return in bank lending. By means of a duration model the factors determining the transition from an active status to dormancy are studied. The estimated model is used to predict the expected durations to dormancy and to analyze the expected profitability for a sample loan applicants. In the fourth paper, the discrete nature of Central Bank interest rate policy is studied. A grouped data model, that can take the long periods of time without changes in the repo rate by the Central Bank into account, is estimated on weekly Swedish data. The model is found to be reasonably good at predicting interest rate changes. / Diss. (sammanfattning) Stockholm : Handelshögsk.

Las tasas brutas de morbi-mortalidad no son indicadores fiables del resultado del producto sanitario, siendo necesario el uso de escalas de riesgo adjustadas por paciente y gravedad. En nuestro estudio se validaron 5 escalas de riesgo para predecir morbilidad y mortalidad en nuestros pacientes sometidos a cirugía programada (POSSUM, P-POSSUM, SAPS II, APACHE II, MODS y MPM). Tras la validación se aplicaron prospectivamente, demostrándose la utilidad de POSSUM y P-POSSUM para predecir morbilidad y mortalidad, por lo que se recomienda su uso en los servicios quirúrgicos. / the use of raw tases to mesuring health results not only is innacuracy but also is dangerous, due to the fact that, the results in medicine depend on the ability of phisician, phisiological status, pre and postoperative cares and kind of intervention. The aim of our study is the validation and prospective aplication of 6 adjusted risk scores (POSSUM, P-POSSUM, SAPS II, APACHE II, MOD S and MPM). We can draw the following conclusions: POSSUM and P-POSSUM are POSSUM and P-POSSUM systems have a high reliability in application for measuring risk of mortality and morbidity in patients studied. APACHE II, SAPS II and MODS scores do not have good reliability in their application for predicting mortality in surgical patients. The use of the POSSUM and P-POSSUM systems is highly recommended in the surgery services to monitor and detect errors in clinical practice.

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Tarptautinių atsiskaitymų banko (BIS) Bazelio II susitarimo nuostatos dėl bankų minimalaus kapitalo apibrėžia reikalavimus kredito rizikos skaičiavimui. Kredito rizikos vertinimo metodai leidžia naudoti vidines įmonių reitingavimo sistemas. Vienas svarbiausių reitingavimo modelio uždavinių – į modelį parinkti tokius finansinius ar nefinansinius rodiklius, kurie geriausiai klasifikuotų įmones pagal jų finansinio pajėgumo lygį. Populiariausias statistinis atrankos rodiklis yra tikslumo koeficientas dar vadinamas Gini indeksu. Tradicinis Gini indeksas buvo apibrėžtas 1914 m. ir iki šiol yra naudojamas pajamų nelygybei populiacijoje apskaičiuoti. 1995 m. Mosleris ir Koševojus pristatė k-matį Gini indekso analogą kaip zonoido tūrį. Šio darbo tikslas – naudojantis zonoidų teorijos idėjomis sukonstruoti apibendrintą reitingavimo modelių Gini indeksą. Pirmoje darbo dalyje pateiktos tradicinės Lorenco kreivės bei Gini indekso sąvokos ir Gini indekso apibendrinimai. Antroje darbo dalyje pagal BIS naudojamas reitingavimo modelio galios sąvokas, apibrėžtas reitingavimo modelio Gini indeksas. Be to, apibrėžti Lorenco kreivės apatinės ir viršutinės aproksimacijų Gini indeksai bei sudaryti šių indeksų apibendrinimai – normos bei tūrio daugiamačiai Gini indeksai. Pabaigoje analizuojamas atskirų finansinių rodiklių Gini indeksų stabilumas bei bendras Gini indeksų – vienamčio, normos ir tūrio – stabilumas ir pateikiamos išvados. / Bank for International Settlements (BIS) Basel II resolutions on banks regulatory capital include requirements for credit risk calculation. Credit risk evaluation methods define the possibility of using the internal rating system. One of the main tasks to build the powerful scoring model is to select financial and non-financial factors that appropriately classify companies according to their financial situation. The most popular statistical measure used for discriminatory analysis is the accuracy ratio or Gini index. General Gini index presented in 1914 is still widely applied to measure income inequality in the population. The k-dimensional analogue of Gini index as volume of zonoid was defined only in 1995 by Mosler and Koshevoy. The main purpose of this paper is to build the generalized Gini index of scoring model following the theory of zonoids. In the first part of the paper the usual Lorenz curve, traditional Gini index and its summary measures are presented. The second part presents the definition of the scoring models Gini index according to scoring model power measures applied in BIS resolutions. Furthermore the Gini indexes of Lorenz curve bottom and top approximations are defined and two its summary measures – norm and volume Gini indexes are constructed. Finally the stability of separate financial ratios Gini indexes and the general stability of univariate, norm and volume Gini indexes are analysed and final conclusions are presented.

This thesis focuses on two statistical problems related to credit scoring. In credit scoring of individuals, two classes are distinguished, namely low and high risk individuals (the so-called "good" and "bad" risk classes). Firstly, we suggest a measure which may be used to study the nature of a classifier for distinguishing between the two risk classes. Secondly, we derive a new method DOUW (detecting outliers using weights) which may be used to fit logistic regression models robustly and for the detection of outliers. In the first problem, the focus is on a measure which may be used to study the nature of a classifier. This measure transforms a random variable so that it has the same distribution as another random variable. Assuming a linear form of this measure, three methods for estimating the parameters (slope and intercept) and for constructing confidence bands are developed and compared by means of a Monte Carlo study. The application of these estimators is illustrated on a number of datasets. We also construct statistical hypothesis to test this linearity assumption. In the second problem, the focus is on providing a robust logistic regression fit and the identification of outliers. It is well-known that maximum likelihood estimators of logistic regression parameters are adversely affected by outliers. We propose a robust approach that also serves as an outlier detection procedure and is called DOUW. The approach is based on associating high and low weights with the observations as a result of the likelihood maximization. It turns out that the outliers are those observations to which low weights are assigned. This procedure depends on two tuning constants. A simulation study is presented to show the effects of these constants on the performance of the proposed methodology. The results are presented in terms of four benchmark datasets as well as a large new dataset from the application area of retail marketing campaign analysis. In the last chapter we apply the techniques developed in this thesis on a practical credit scoring dataset. We show that the DOUW method improves the classifier performance and that the measure developed to study the nature of a classifier is useful in a credit scoring context and may be used for assessing whether the distribution of the good and the bad risk individuals is from the same translation-scale family. / Thesis (Ph.D. (Risk Analysis))--North-West University, Potchefstroom Campus, 2008.

This study aimed to develop and validate a rating scale for assessing English First Additional Language essays at Grade 12 level for the final National Senior Certificate examination. The importance of writing as a communicative skill is emphasised with the re-introduction of writing as Paper 3 of the English First Additional Language examination at the end of Grade 12 in South Africa. No empirical evidence, however, is available to support claims of validity for the current rating scale. The literature on the concept of validity and the process of validation was surveyed. Theoretical models and validation frameworks were evaluated to establish a theoretical base for the development and validation of a rating scale for assessing writing. The adopted framework was used to evaluate the adequacy of the current rating scale used for assessing Grade 12 writing in South Africa. The current scale was evaluated in terms of the degree to which it offers an appropriate means of assessing Grade 12 Level essay writing while adhering to requirements of the National Curriculum Statement. It was found lacking and the need for a new, validated rating scale was established. Various approaches to scale development were considered in consideration of factors that impact scores directly, viz. the type of rating scale, rater characteristics, scoring procedures and rater training. A new scale was developed and validated following an empirical procedure comprising four phases. The empirical process was based on an analysis of actual performances of Grade 12 English learner writing. A combination of quantitative and qualitative methods was used in each of the four phases to ensure the validity of the instrument. The outcome of this project was an empirically developed and validated multiple trait rating scale to assess Grade 12 essay writing. The proposed scale distinguishes five criteria assessed by means of a seven-point scale. / Thesis (Ph.D. (English))--North-West University, Potchefstroom Campus, 2009.

This thesis focuses on two statistical problems related to credit scoring. In credit scoring of individuals, two classes are distinguished, namely low and high risk individuals (the so-called "good" and "bad" risk classes). Firstly, we suggest a measure which may be used to study the nature of a classifier for distinguishing between the two risk classes. Secondly, we derive a new method DOUW (detecting outliers using weights) which may be used to fit logistic regression models robustly and for the detection of outliers. In the first problem, the focus is on a measure which may be used to study the nature of a classifier. This measure transforms a random variable so that it has the same distribution as another random variable. Assuming a linear form of this measure, three methods for estimating the parameters (slope and intercept) and for constructing confidence bands are developed and compared by means of a Monte Carlo study. The application of these estimators is illustrated on a number of datasets. We also construct statistical hypothesis to test this linearity assumption. In the second problem, the focus is on providing a robust logistic regression fit and the identification of outliers. It is well-known that maximum likelihood estimators of logistic regression parameters are adversely affected by outliers. We propose a robust approach that also serves as an outlier detection procedure and is called DOUW. The approach is based on associating high and low weights with the observations as a result of the likelihood maximization. It turns out that the outliers are those observations to which low weights are assigned. This procedure depends on two tuning constants. A simulation study is presented to show the effects of these constants on the performance of the proposed methodology. The results are presented in terms of four benchmark datasets as well as a large new dataset from the application area of retail marketing campaign analysis. In the last chapter we apply the techniques developed in this thesis on a practical credit scoring dataset. We show that the DOUW method improves the classifier performance and that the measure developed to study the nature of a classifier is useful in a credit scoring context and may be used for assessing whether the distribution of the good and the bad risk individuals is from the same translation-scale family. / Thesis (Ph.D. (Risk Analysis))--North-West University, Potchefstroom Campus, 2008.