Physical Sciences and Mathematics Commons^™

Evaluation Of The Effect Of The Clinical-Decision-Support Systems On Diabetes Management: A Multivariate Meta-Analysis Comparison With Univariate Meta-Analysis, Abdelfattah Elbarsha

Electronic Theses and Dissertations

The advantage of using meta-analysis lies in its ability in providing a quantitative summary of the findings from multiple studies. The aim of this dissertation was first to conduct a simulation study in order to understand what factors (sample size, between-study correlation, and percent of missing data) have a significant effect on meta-analysis estimates and whether using univariate or multivariate meta-analysis would produce different estimates.

The second goal of this study was to evaluate the effect of clinical decision support systems CDSS on diabetes care management by conducting three separate univariate meta-analyses and one multivariate meta-analysis. CDSS are health information …

The Combined Impact Of Continuous And Ordinal Auxiliary Variables On Missing Data Imputation In Sem, Salina Wu Whitaker

Electronic Theses and Dissertations

“Modern” methods of addressing missing data using full-information maximum-likelihood (FIML) have become mainstays in SEM analyses. FIML allows the inclusion of auxiliary variables which carry information that is related to missing values and can reduce bias in parameter estimates. Past research has illustrated the benefits of auxiliary variable inclusion under different missingness conditions (MCAR and MNAR; e.g., Enders, 2008), missingness proportions (e.g., Collins et al., 2001), and although limited, missingness patterns (e.g., Yoo, 2009) in FIML analyses. While past studies have focused on the effects of either continuous or ordinal auxiliary variables, no study has included both types in their …

Power And Statistical Significance In Securities Fraud Litigation, Jill E. Fisch, Jonah B. Gelbach

All Faculty Scholarship

Event studies, a half-century-old approach to measuring the effect of events on stock prices, are now ubiquitous in securities fraud litigation. In determining whether the event study demonstrates a price effect, expert witnesses typically base their conclusion on whether the results are statistically significant at the 95% confidence level, a threshold that is drawn from the academic literature. As a positive matter, this represents a disconnect with legal standards of proof. As a normative matter, it may reduce enforcement of fraud claims because litigation event studies typically involve quite low statistical power even for large-scale frauds.

This paper, written for …

Ensemble Protein Inference Evaluation, Kyle Lee Lucke

Graduate Student Theses, Dissertations, & Professional Papers

The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in …

Statistical Modeling Of Hpc Performance Variability And Communication, Jered B. Dominguez-Trujillo

Computer Science ETDs

Understanding the performance of parallel and distributed programs remains a focal point in determining how compute systems can be optimized to achieve exascale performance. Lightweight, statistical models allow developers to both characterize and predict performance trade-offs, especially as HPC systems become more heterogeneous with many-core CPUs and GPUs. This thesis presents a lightweight, statistical modeling approach of performance variation which leverages extreme value theory by focusing on the maximum length of distributed workload intervals. This approach was implemented in MPI and evaluated on several HPC systems and workloads. I then present a performance model of partitioned communication which also uses …

Computational Simulation And Analysis Of Neuroplasticity, Madison E. Yancey

Browse all Theses and Dissertations

Homeostatic synaptic plasticity is the process by which neurons alter their activity in response to changes in network activity. Neuroscientists attempting to understand homeostatic synaptic plasticity have developed three different mathematical methods to analyze collections of event recordings from neurons acting as a proxy for neuronal activity. These collections of events are from control data and treatment data, referring to the treatment of neuron cultures with pharmacological agents that augment or inhibit network activity. If the distribution of control events can be functionally mapped to the distribution of treatment events, a better understanding of the biological processes underlying homeostatic synaptic …

Assessing And Forecasting Chlorophyll Abundances In Minnesota Lake Using Remote Sensing And Statistical Approaches, Ben Von Korff

All Graduate Theses, Dissertations, and Other Capstone Projects

Harmful algae blooms (HABs) can negatively impact water quality, lake aesthetics, and can harm human and animal health. However, monitoring for HABs is rare in Minnesota. Detecting blooms which can vary spatially and may only be present briefly is challenging, so expanding monitoring in Minnesota would require the use of new and cost efficient technologies. Unmanned aerial vehicles (UAVs) were used for bloom mapping using RGB and near-infrared imagery. Real time monitoring was conducted in Bass Lake, in Faribault County, MN using trail cameras. Time series forecasting was conducted with high frequency chlorophyll-a data from a water quality sonde. Normalized …

Improving The Data Quality In Gravitation-Wave Detectors By Mitigating Transient Noise Artifacts, Kentaro Mogushi

Doctoral Dissertations

“The existence of gravitational waves (GWs), small perturbations in spacetime produced by accelerating massive objects was first predicted in 1916 as solutions of Einstein’s Theory of General Relativity (Einstein, 1916). Detecting and analyzing GWs produced by sources allows us to probe astrophysical phenomena.

The era of GW astronomy began from the first direct detection of the coalescence of a binary black hole in 2015 by the collaboration of the advanced Laser Interferometer Gravitational-wave Observatory (LIGO) (Aasi et al., 2015) and advanced Virgo (Abbott et al., 2016a). Since 2015, LIGO-Virgo detected about 50 confident transient events of GW signals (Abbott et …

Bickel-Rosenblatt Test Based On Tilted Estimation For Autoregressive Models & Deep Merged Survival Analysis On Cancer Study Using Multiple Types Of Bioinformatic Data, Yan Su

Browse all Theses and Dissertations

This dissertation includes two topics, Bickel-Rosenblatt test based on tilted density estimation for autoregressive models and deep merged survival analysis on cancer study using multiple types of bioinformatic data. In the first topic study, we consider the goodness of fit test the error density of linear and nonlinear autoregressive models using tilted kernel density estimation based on residuals. Bickel-Rosenblatt test statistic is based on the integrated square error of non-parametric error density estimation and a smoothed version of the parametric fit of the density. It is shown that the new type of Bickel-Rosenblatt test statistics behaves asymptotically the same as …