Enrollment in degree-granting postsecondary institutions in the U.S. is increasing, as are the numbers of students entering academically underprepared. Students in remedial mathematics represent the largest percentage of total enrollment in remedial courses, and national statistics indicate that less than half of these students pass all of the remedial math courses in which they enroll. In response to the low pass rates, numerous studies have been conducted into the use of alternative modes of instruction to increase passing rates. Despite myriad studies into course redesign, passing rates have seen no large-scale improvement. Lacking is a thorough investigation into preexisting differences between students who do and do not take remedial math. My study examined the effect of taking remedial math courses in college on degree attainment and college GPA using a subsample of the Educational Longitudinal Study of 2002. This nonexperimental study examined preexisting differences between students who did and did not take remedial math. The study incorporated propensity score matching, a statistical analysis not commonly used in educational research, to create comparison groups of matched students using multiple covariate measures. Missing value analyses and multiple imputation procedures were also incorporated as methods for identifying and handling missing data. Analyses were conducted on both matched and unmatched groups, as well as on 12 multiply imputed data sets. Binary logistic regression analyses showed that preexisting differences between students on academic, nonacademic, and non-cognitive measures significantly predicted remedial math-taking in college. Binary logistic regression analyses also indicated that students who did not take remedial math courses in college were 1.5 times more likely to earn a degree than students who took remedial math. Linear regression analyses showed that taking remedial math had a significant negative effect on mean college GPA. Students who did not take remedial math had a higher mean GPA than students who did take remedial math. These results were consistent across unmatched groups, matched groups, and all 12 multiply imputed data sets.

Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods that are inefficient and arguably inappropriate, such as repeated measures analysis of variance (RM-ANOVA). Newer statistical approaches may offer substantial gains in accuracy and precision of parameter estimation and power. We propose an alternative approach to modeling single-peaked, longitudinal EI data that incorporates recent developments in nonlinear hierarchical models and Bayesian statistics. We begin by introducing a nonlinear mixed model (NLMM) for a symmetric infection response variable. We employ a standard NLMM assuming normally distributed errors and a Gaussian mean response function. The parameters of the model correspond directly to biologically meaningful properties of the infection response, including baseline, peak intensity, time to peak and spread. Through Monte Carlo simulation studies we demonstrate that the model outperforms RM-ANOVA on most measures of parameter estimation and power. Next we generalize the symmetric NLMM to allow modeling of variables with asymmetric time course. We implement the asymmetric model as a Bayesian nonlinear hierarchical model (NLHM) and discuss advantages of the Bayesian approach. Two illustrative applications are provided. Finally we consider modeling of viral load. For several reasons, a normal-errors model is not appropriate for viral load. We propose and illustrate a Bayesian NLHM with the individual responses at each time point modeled as a Poisson random variable with the means across time points related through a Tricube mean response function. We conclude with discussion of limitations and open questions, and a brief survey of broader applications of these models.

Ovarian cancer (OC) is the fifth most common cancer among women and has the highest mortality of any cancer of the female reproductive system. The majority (61%) of OC cases are diagnosed at a distant stage. Because diagnoses occur most commonly at a late-stage and prognosis for advanced disease is poor, research focusing on the development of effective OC screening methods to facilitate early detection in high-risk, asymptomatic women is fundamental in reducing OC-specific mortality. Presently, there is no screening modality proven efficacious in reducing OC-mortality. However, transvaginal ultrasonography (TVS) has shown value in early detection of OC. TVS presents with the possibility of false positive results which occur when a women receives an abnormal TVS screening test result that is deemed benign following repeat testing (about 7% of the time). The purpose of this dissertation was to evaluate the impact of false positive TVS screening test results on a variety of psychological and behavioral outcomes using mixed and trajectory statistical modeling. The three specific aims of this dissertation were to 1) compare psychological and behavioral outcomes between women receiving normal and false positive results, 2) identify characteristics of women receiving false positive results associated with increased OC-specific distress and 3) characterize distress trajectories following receipt of false positive results. Analyses included a subset of women participating in an experimental study conducted through the University of Kentucky Ovarian Cancer Screening Program. 750 women completed longitudinal assessments: 375 false positive and 375 normal results. Mixed and group-based trajectory modeling were used to evaluate the specific aims. Results suggest women receiving false positive TVS result experience increased OC-specific distress compared to women receiving normal results. Among those receiving false positives, less education, no history of an abnormal screening test result, less optimism and more social constraint were associated with increased OC-specific distress. Family history was associated with increased distress among women with monitoring informational coping styles. Three distinct trajectories characterize the trajectory of distress over a four-month study period. Although decreasing over time, a notable proportion of women experience sustained high levels of OC-specific distress.

Para qualquer sistema observado, físico ou qualquer outro, geralmente se deseja fazer predições para sua evolução futura. Algumas vezes, muito pouco é conhecido sobre o sistema. Se uma série temporal é a única fonte de informação no sistema, predições de valores futuros da série requer uma modelagem da lei da dinâmica do sistema, talvez não linear. Um interesse em particular são as capacidades de previsão do modelo global para análises de séries temporais. Isso pode ser um procedimento muito complexo e computacionalmente muito alto. Nesta dissertação, nos concetraremos em um determinado caso: Em algumas situações, a única informação que se tem sobre o sistema é uma série sequencial de dados (ou série temporal). Supondo que, por detrás de tais dados, exista uma dinâmica de baixa dimensionalidade, existem técnicas para a reconstrução desta dinâmica.O que se busca é desenvolver novas técnicas para poder melhorar o poder de previsão das técnicas já existentes, através da programação computacional em Maple e C/C++.

Para qualquer sistema observado, físico ou qualquer outro, geralmente se deseja fazer predições para sua evolução futura. Algumas vezes, muito pouco é conhecido sobre o sistema. Se uma série temporal é a única fonte de informação no sistema, predições de valores futuros da série requer uma modelagem da lei da dinâmica do sistema, talvez não linear. Um interesse em particular são as capacidades de previsão do modelo global para análises de séries temporais. Isso pode ser um procedimento muito complexo e computacionalmente muito alto. Nesta dissertação, nos concetraremos em um determinado caso: Em algumas situações, a única informação que se tem sobre o sistema é uma série sequencial de dados (ou série temporal). Supondo que, por detrás de tais dados, exista uma dinâmica de baixa dimensionalidade, existem técnicas para a reconstrução desta dinâmica.O que se busca é desenvolver novas técnicas para poder melhorar o poder de previsão das técnicas já existentes, através da programação computacional em Maple e C/C++.

This thesis gives an overview of the principles of a points system for evaluating the drivers (point system) and statistics of accidents in road transport of the Czech Republic.. The operating principle of the points system is supplemented by graphical analysis of drivers who have received some penalty point and also overview of European countries with similar systems. The thesis contains the results of public research on the topic "point system". At the conclusion of this thesis is an analysis of time series of basic indicators of accidents on the roads of the Czech Republic supplemented by analysis of the impact point system for accident and its consequences on people's lives.

Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions in settings with severely underreported data. We compare penalized regression approaches to seasonal baseline models and illustrate that incorporation of search data can improve prediction error. This builds on previous research show- ing that search data and recent surveillance data together can be used to create accurate forecasts for diseases such as influenza and dengue fever. This work shows that even in settings where timely surveillance data is not available, using search data in real-time can produce more accurate short-term forecasts than a seasonal baseline prediction. However, forecast accuracy degrades the further into the future the forecasts go. The relative accuracy of these forecasts compared to a seasonal average forecast varies depending on location. Overall, these data and models can improve short-term public health situational awareness and should be incorporated into larger real-time forecasting efforts.

The capability to be able to determine the future progression on the worlds stock exchange is an important issue, which has become discernible in the last decades. An important role of this progression lies within the fast advancements in computerized technology. Aforementioned document describes a mechanism used for prediction of the future price of a certain stock. The strategy of trading is build upon this mechanism, and the core of this prediction system is an artificial neural network. Inputs used in this network are indicators derived from technical analysis. This trading system was implemented into historical trades and successfully tested.

This thesis deals with an echo state network and with acceleration of its learning by implementing the echo state network on a graphics processor. The theoretical part consists of the description of neural networks and some selected types of neural networks, on which is based the echo state network. After that, there are some other algorithms described used for time series analysis and last but not least, the tools that were used in the practical part of the thesis were briefly described. The practical part describes the creation of the accelerated version of the echo state network. After that, there is described the creation of input data sets of real financial indexes, on which the echo state network and the other algorithmns were then tested. By analyzing this accelerated version it was found that its learning speed did not reach the theoretical expectations. The accelerated version works slower, but with greater precision. By analyzing the results of the measurement of the other algorithmns it was found that the highest precision is achieved by solutions based on the neural network principle.

The capability to be able to determine the future progression on the worlds stock exchange is an important issue, which has become discernible in the last decades. An important role of this progression lies within the fast advancements in computerized technology.Aforementioned document describes a mechanism used for prediction of the future price of a certain stock. The strategy of trading is build upon this mechanism, and the core of this prediction system is an artificial neural network. Inputs used in this network are indicators derived from technical analysis. This trading system was implemented into historical trades and successfully tested.