This thesis has investigated two-stage regularized logistic regressions applied on the credit scoring problem. Credit scoring refers to the practice of estimating the probability that a customer will default if given credit. The data was supplied by Klarna AB, and contains a larger number of observations than many other research papers on credit scoring. In this thesis, a two-stage regression refers to two staged regressions were the some kind of information from the first regression is used in the second regression to improve the overall performance. In the best performing models, the first stage was trained on alternative labels, payment status at earlier dates than the conventional. The predictions were then used as input to, or to segment, the second stage. This gave a gini increase of approximately 0.01. Using conventional scorecutoffs or distance to a decision boundary to segment the population did not improve performance. / Denna uppsats har undersökt tvåstegs regulariserade logistiska regressioner för att estimera credit score hos konsumenter. Credit score är ett mått på kreditvärdighet och mäter sannolikheten att en person inte betalar tillbaka sin kredit. Data kommer från Klarna AB och innehåller fler observationer än mycket annan forskning om kreditvärdighet. Med tvåstegsregressioner menas i denna uppsats en regressionsmodell bestående av två steg där information från det första steget används i det andra steget för att förbättra den totala prestandan. De bäst presterande modellerna använder i det första steget en alternativ förklaringsvariabel, betalningsstatus vid en tidigare tidpunkt än den konventionella, för att segmentera eller som variabel i det andra steget. Detta gav en giniökning på approximativt 0,01. Användandet av enklare segmenteringsmetoder så som score-gränser eller avstånd till en beslutsgräns visade sig inte förbättra prestandan.

Hydrogen production by means of anaerobic fermentation was researched utilizing three different substrates. Synthetic wastewater, dairy manure, and cheese whey were combined together at different concentrations under batch anaerobic conditions to determine the optimal hydrogen producing potential and waste treatment of each. Cheese whey at a concentration of 55% was combined with dairy manure at a concentration of 45% to produce 1.53 liters of hydrogen per liter of substrate. These results are significant because the control, synthetic wastewater, which was a glucose-based substrate, produced less hydrogen, 1.34 liters per liter of substrate, than the mixture of cheese whey and dairy manure. These findings indicate that cheese whey and dairy manure, which are of little value, have potential to produce clean combusting hydrogen fuel. The effluent from the anaerobic hydrogen fermentations was then placed into a second continuous-fed reactor as part of a two-phase anaerobic digestion system. This system was designed to produce hydrogen and methane for a mixture of approximately 10% hydrogen. The two-stage process also further treated the synthetic wastewater, dairy manure, and cheese whey. The two-phase anaerobic methanogenic reactor was shown to produce more methane in the second phase (56 L IBR anaerobic digester), 1.36 mL per minute per liter substrate, as compared to the single-phase anaerobic reactor (56 L IBR), which produced 1.22 mL per minute per liter substrate. In general, this research has suggested that agricultural and food processing wastes provide the needed nutrients for hydrogen production and that a two-phase anaerobic digestion system is ideally set up to produce hydrogen-methane mixtures while treating wastes for discharge into the environment.

Indiana University-Purdue University Indianapolis (IUPUI) / Adverse drug events (ADEs) are injuries resulting from drug-related medical interventions. ADEs can be either induced by a single drug or a drug-drug interaction (DDI). In order to prevent unnecessary ADEs, many regulatory agencies in public health maintain pharmacovigilance databases for detecting novel drug-ADE associations. However, pharmacovigilance databases usually contain a significant portion of false associations due to their nature structure (i.e. false drug-ADE associations caused by co-medications). Besides pharmacovigilance studies, the risks of ADEs can be minimized by understating their mechanisms, which include abnormal pharmacokinetics/pharmacodynamics due to genetic factors and synergistic effects between drugs. During the past decade, pharmacogenomics studies have successfully identified several predictive markers to reduce ADE risks. While, pharmacogenomics studies are usually limited by the sample size and budget. In this dissertation, we develop statistical methods for pharmacovigilance and pharmacogenomics studies. Firstly, we propose an empirical Bayes mixture model to identify significant drug-ADE associations. The proposed approach can be used for both signal generation and ranking. Following this approach, the portion of false associations from the detected signals can be well controlled. Secondly, we propose a mixture dose response model to investigate the functional relationship between increased dimensionality of drug combinations and the ADE risks. Moreover, this approach can be used to identify high-dimensional drug combinations that are associated with escalated ADE risks at a significantly low local false discovery rates. Finally, we proposed a cost-efficient design for pharmacogenomics studies. In order to pursue a further cost-efficiency, the proposed design involves both DNA pooling and two-stage design approach. Compared to traditional design, the cost under the proposed design will be reduced dramatically with an acceptable compromise on statistical power. The proposed methods are examined by extensive simulation studies. Furthermore, the proposed methods to analyze pharmacovigilance databases are applied to the FDA’s Adverse Reporting System database and a local electronic medical record (EMR) database. For different scenarios of pharmacogenomics study, optimized designs to detect a functioning rare allele are given as well.

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No description available.

The removal of 34 per- and polyfluoroalkyl substances (PFAS) were compared in a 1 stagegranular activated carbon (GAC) filtration to a 2 stage GAC filtration in a pilot study at adrinking water treatment plant (DWTP). The PFASs that were present in the water wereperfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluoropentanoic acid(PFPeA), perfluorohexanoic acid (PFHxA), perfluoroheptanoic acid (PFHpA), perfluorobutanesulfonic acid (PFBS), perfluoropentane sulfonic acid (PFPeS) and perfluorohexane sulfonic acid(PFHxS). A cost comparison for the operation of a one stage GAC to a two stage GAC wascompared for PFAS4 (sum of PFOA, PFNA, PFHxS and PFOS) at treatment goals ranging from2 - 10 ng/L. The pilot was operated at three different flows and the three different bed volumes(BV)s resulting in three different empty bed contact times (EBCTs) at three different times.Therefore, the Lin & Huang adsorption model (1999) was used to model the concentrations ateach EBCT. It was found that the model worked good for PFBS, PFPeS, PFHxS, PFOS andPFOA but not for PFPeA, PFHxA and PFHpA (except for PFPeA and PFHxA during EBCT 5min) and did not work for desorbing PFASs. The removal comparison of PFASs was made,partly by comparing removal efficiencies between the first stage and the second stage GAC filterand by comparing the removal per weight of GAC per BV 1 stage and 2 stages. It was found thatthe removal efficiency decreases with decreasing chain length and increasing treated BVs forboth the first stage and the second stage. The short chain PFCAs were also desorbing after anumber of BVs. The removal per weight of GAC showed that the removal does not increasewhen comparing a one stage GAC to a two stage GAC for any the PFAS. The cost comparisonwas made using the adsorption model. It showed that it was cheaper to operate a 2 stage GAC forthe EBCT of 5 minutes and 8 minutes for the whole range of treatment goals. However, for theEBCT of 15 minutes the costs for the second stage was decreasing with decreasing treatmentgoal which is unrealistic result. This was caused by too few data points available for the model topredict reliable results.

No description available.

Natural rubber is a major export crop and the sector is an important source of employment in Thailand. Very few rubber studies in the past have examined the demand and supply equations simultaneously and the previously results are dated. The objectives of this study was to estimate the econometric model of demand and supply of natural rubber in Thailand and determine if a relationship exists between the supply of rubber and its determinants. The data contained in the study are secondary time series annual data from 1977-2012. The instrumental variables estimation by two-stage least squares was used to solve and analyze the demand and supply of rubber. Results were statistically significant at 0.01 level, which showed that the U.S. GDP per capita, the estimated price, rainfall and rice price have a significant effect on quantity of rubber production in Thailand with an estimated elasticity of 1.4, 3.3, -3.6 and -2.6, respectively. The implications of the results are assessed through the lens of rubber producers, rubber consumers and agricultural policy makers.

In this work a proof of concept for a step-down DC-DC converter used in a high voltage submarine application is presented. The purpose of the converter is to step down a 5000V-6000V input to a 24V output which can serve as an input to a submarine sensor. The completed system consists of two stages where the first stage is an unregulated switched capacitor converter to step down the initial input to a voltage range more appropriate for the selected second stage. The second stage is a regulated flyback converter topology which regulates the final output to the desired 24V. Performance evaluation of the proposed system are carried out using LTspice simulation software. Results of the simulation demonstrate that the proposed converter operates as anticipated with the first stage being able to reduce the initial input by a factor of 16 and the second stage producing a regulated 24V output. Additionally, the proposed converter reaches an efficiency of approximately 74.95% when tested under nominal input and full load conditions. With the same conditions, the converter yields an output voltage ripple of 1.525%, and line and load regulations of 0.0457% and 0.183% respectively.

Traditional single stage design optimality procedures can be used to efficiently generate data for an assumed model y = f(x^(m),b) + ε. The model assumptions include the form of f, the set of regressors, x^(m) , and the distribution of ε. The nature of the response, y, often provides information about the model form (f) and the error distribution. It is more difficult to know, apriori, the specific set of regressors which will best explain the relationship between the response and a set of design (control) variables x. Misspecification of x^(m) will result in a design which is efficient, but for the wrong model. A Bayesian two stage design approach makes it possible to efficiently design experiments when initial knowledge of x^(m) is poor. This is accomplished by using a Bayesian optimality criterion in the first stage which is robust to model uncertainty. Bayesian analysis of first stage data reduces uncertainty associated with x^(m), enabling the remaining design points (second stage design) to be chosen with greater efficiency. The second stage design is then generated from an optimality procedure which incorporates the improved model knowledge. Using this approach, numerous two stage design procedures have been developed for the normal linear model. Extending this concept, a Bayesian design augmentation procedure has been developed for the purpose of efficiently obtaining data for variance modeling, when initial knowledge of the variance model is poor. / Ph. D.