Proximity scaling methods (e.g., multidimensional scaling) represent objects in a low dimensional configuration so that fitted distances between objects optimally approximate multivariate proximities. Next to finding the optimal configuration the goal is often also to assess groups of objects from the configuration. This can be difficult if the optimal configuration lacks clusteredness (coined c-clusteredness). We present Cluster Optimized Proximity Scaling (COPS), which attempts to solve this problem by finding a configuration that exhibts c-clusteredness. In COPS, a flexible scaling loss function (p-stress) is combined with an index that quantifies c-clusteredness in the solution, the OPTICS Cordillera. We present two variants of combining p-stress and Cordillera, one for finding the configuration directly and one for metaparameter selection for p-stress. The first variant is illustrated by scaling Californian counties with respect to climate change related natural hazards. We identify groups of counties with similar risk profiles and find that counties that are in high risk of drought are socially vulnerable. The second variant is illustrated by finding a clustered nonlinear representation of countries according to their history of banking crises from 1800 to 2010. (authors' abstract) / Series: Discussion Paper Series / Center for Empirical Research Methods

The postsecondary enrollment of student veterans has increased with the troop draw down in Iraq and Afghanistan as well as the generous amendments made to the Post 9/11 GI Bill. Acquired disabilities remain a reality for this population as they transition into the civilian world; consequently, previous literature cites the role of disabilities amongst student veterans. Also, prior research often aggregates these two groups without a thorough understanding of the ways in which they differ. This study compared student veterans with disabilities to student veterans without disabilities in order to understand the enrollment and demographic factors on which they differed, if any. Using a secondary data analysis of the 2007-2008 National Postsecondary Student Aid Survey, univariate tests of significance, a logistic regression, and a discriminant function analysis examined the relationship between disability status and seven predictor variables: age, gender, GPA, major, risk index, degree program type, and whether or not a student was exclusively a distance learner. These seven variables as a whole were not significant predictors of disability status; however, the models provided valuable insight into the similarities and characteristics shared within this population. Univariate tests of significance revealed that students with disabilities had a significantly lower mean GPA, were more often male, tended to favor certain academic majors over others, more often enrolled in bachelor’s degree versus associate and certificate programs, and had a lower risk of attrition based on their index of risk. Major, degree program type, and risk index proved to be the most significant predictors of disability status in LR and DFA. A student veteran’s age and whether they were a distance learner had no significant bearing on disability status indicating that student veterans enroll in distance learning or campus-based programs without influence from an orthopedic or mobility impairment, the most common type of disability amongst student veterans. This study offers a full description of student veterans with disabilities including the specific types of disabilities with which this population enters higher education.

Latent variable models (LVMs) are commonly used in the scenario where the outcome of the main interest is an unobservable measure, associated with multiple observed surrogate outcomes, and affected by potential risk factors. This thesis develops an approach of efficient handling missing surrogate outcomes and covariates in two- and three-level latent variable models. However, corresponding statistical methodologies and computational software are lacking efficiently analyzing the LVMs given surrogate outcomes and covariates subject to missingness in the LVMs. We analyze the two-level LVMs for longitudinal data from the National Growth of Health Study where surrogate outcomes and covariates are subject to missingness at any of the levels. A conventional method for efficient handling of missing data is to reexpress the desired model as a joint distribution of variables, including the surrogate outcomes that are subject to missingness conditional on all of the covariates that are completely observable, and estimate the joint model by maximum likelihood, which is then transformed to the desired model. The joint model, however, identifies more parameters than desired, in general. The over-identified joint model produces biased estimates of LVMs so that it is most necessary to describe how to impose constraints on the joint model so that it has a one-to-one correspondence with the desired model for unbiased estimation. The constrained joint model handles missing data efficiently under the assumption of ignorable missing data and is estimated by a modified application of the expectation-maximization (EM) algorithm.

This study utilized a mixed methods design. The study was carried out in two phases. In the first phase of the study, a secondary data analysis of data were collected from a sample (n = 390) of middle and high school students who participated in a truancy pilot program launched during the 2012-2013 school year with follow-up services provided through June 2014. The sample was divided into two groups (treatment and control). The treatment group was diverted from court referral and participated in an intervention consisting of in-home counseling and case management services. The control group was referred to court and went through the traditional court process and received no treatment services. The effectiveness of the intervention was measured through the collection of pre and post intervention data consisting of the number of unexcused absences, disciplinary referrals, beginning and final grades in English, math, science, and social studies. As a final variable, retention and promotion rates were examined. The effectiveness of the truancy reduction intervention was measured by the amount of reduction in these variables following the implementation of the treatment. Data in the first phase of the study were collected by the Family Assessment and Planning Team (FAPT) in partnership with the school district and other agencies. Further analysis was performed in Phase II of the study utilizing a single school case study design. Qualitative case study is an approach to research that allows the researcher to explore a phenomenon within its context using a variety of data sources. For this phase of the study, data were collected through a survey and a focus group using a sample of students from the treatment and control group of the truancy pilot program. The focus group was designed to gain insight from the voices of the students regarding their perceptions of the factors that influence truancy and the effectiveness of truancy intervention. The statistical procedures used to examine the quantitative data included Analysis of Covariance (ANCOVA) and Chi Square. Analysis of data collected in Phase I of the study revealed that there was no difference in the effect of treatment for students who were diverted from court to treatment services and those who went through the traditional court process and received no treatment. This finding was supported by results of the analysis of data from the survey and focus group. Results indicated that students did not perceive either invention as being more effective than the other in reducing their truancy. Further, results of the survey and focus group indicated that school factors, not family factors, had the greatest impact on the students’ nonattendance. School factors such as safety, teacher and student relationships, and teacher expectations were identified as primary themes. The findings suggest that the truancy pilot intervention’s focus on family factors as a means of reducing chronic truancy may have been focused in the wrong direction. .

Recent advances in computing technology have lead to the development of algorithmic modeling techniques. These methods can be used to analyze data which are difficult to analyze using traditional statistical models. This study examined the effectiveness of variable importance estimates from the random forest algorithm in identifying the true predictor among a large number of candidate predictors. A simulation study was conducted using twenty different levels of association among the independent variables and seven different levels of association between the true predictor and the response. We conclude that the random forest method is an effective classification tool when the goals of a study are to produce an accurate classifier and to provide insight regarding the discriminative ability of individual predictor variables. These goals are common in gene expression analysis, therefore we apply the random forest method for the purpose of estimating variable importance on a microarray data set.

This thesis is focused on analysis of youth delinquency in terms of Per-Olof H. Wikström's Situational Action Theory or rather modelling data relating to this area of research International Self-Report Delinquency Study 3. The main aim of the thesis is to introduce and evaluate this theoretical-empirical model for the explanation of youth delinquency. The work is split into three main parts - theoretical, methodological and empirical. First one consists of the definition of basic concepts and show Wikström' s 'situational action theory applied to the delinquency topic. In methodological part there is a description of ISRD-3 survey, basic indicators of sample and data collection methods used. And then there is an explanation how operationalization of individual explanatory variables in the model was done. Empirical part is dedicated to multidimensional analysis of data and evaluation of this concept. The results demonstrate the success of the analytical model and its application as a default theory in the examination of youth delinquency.

The global optimization problem -- i.e., the problem of finding global extreme points of given function on restricted domain of values -- often appears in many real-world applications. Improving efficiency of this task can reduce the latency of the application or provide more precise result since the task is usually solved by an approximative algorithm. This thesis focuses on the practical aspects of global optimization algorithms, especially in the domain of algorithmic trading data analysis. Successful implementations of the global optimization solver already exist for CPUs, but they are quite time demanding. The main objective of this thesis is to design a GO solver that utilizes the raw computational power of the GPU devices. Despite the fact that the GPUs have significantly more computational cores than the CPUs, the parallelization of a known serial algorithm is often quite challenging due to the specific execution model and the memory architecture constraints of the existing GPU architectures. Therefore, the thesis will explore multiple approaches to the problem and present their experimental results.

Determinants of financial development Abstract The paper studies effects of country level determinants on the rate of financial development and, in particular, assesses the empirical question whether democracy and political freedom can enhance financial development, as measured by Bank Private Credit to GDP and Liquid Liabilities to GDP. Using Fixed Effects estimation techniques and a panel data for a list of 39 countries over the period 1990 to 2011, we provide evidence that suggests positive link between political openness and financial development. The empirical evidence also confirms financial openness and real per capita income to be positively correlated to financial deepening and in contrast, we find that size of financial sector does not spur the rate of financial development.

This thesis is focused on analysis of youth delinquency in terms of Per-Olof H. Wikström's Situational Action Theory or rather modelling data relating to this area of research International Self-Report Delinquency Study 3. The main aim of the thesis is to introduce and evaluate this theoretical-empirical model for the explanation of youth delinquency. The work is split into three main parts - theoretical, methodological and empirical. First one consists of the definition of basic concepts and show Wikström' s 'situational action theory applied to the delinquency topic. In methodological part there is a description of ISRD-3 survey, basic indicators of sample and data collection methods used. In empirical part is an explanation how operationalization of individual explanatory variables in the model was done. This part is also dedicated to multidimensional analysis of data and evaluation of this concept. The results demonstrate the success of the analytical model and its application as a default theory in the examination of youth delinquency. Key words: youth delinquency, Situational Action Theory, multidimensional data analysis

This thesis deals with the description of $\gamma-$ray deexcitation of neutron resonances produced in thermal neutron capture below neutron separation energy. A subject of this thesis is obtaining information on absolute value of \textit{photon strength function} (PSF) achieved from primary transitions in thermal neutron capture. The aim is to map and bring new information on absolute value of photon strength function (PSF) in $^{156}$Gd and $^{158}$Gd. The method which was used in this thesis can lead to refusion of several models of PSF a level density. Powered by TCPDF (www.tcpdf.org)