Effects Of Perioperative Hyperglycemia In Patients With Diabetes Compared To Patients Without Diabetes: A Retrospective Study Of Treatment And Outcomes,
2019
University of Nebraska Medical Center
Effects Of Perioperative Hyperglycemia In Patients With Diabetes Compared To Patients Without Diabetes: A Retrospective Study Of Treatment And Outcomes, Matthew Anderson
Capstone Experience: Master of Public Health
The main goal of this project was to examine the differences in perioperative hyperglycemia treatment received by patients with a diagnosis of diabetes mellitus (DM) and patients without a diagnosis of diabetes (NDM); and how these treatment differences can affect the length of hospital stay. Studies have revealed that, when comparing DM and NDM patients with the same degree of perioperative hyperglycemia, NDM patients suffer worse outcomes. It has been suggested in previous research that this may be because NDM patients receive treatment that does not measure up to the standard of care treatment that DM patients receive. In this …
Generalizations Of The Arcsine Distribution,
2019
East Tennessee State University
Generalizations Of The Arcsine Distribution, Rebecca Rasnick
Electronic Theses and Dissertations
The arcsine distribution looks at the fraction of time one player is winning in a fair coin toss game and has been studied for over a hundred years. There has been little further work on how the distribution changes when the coin tosses are not fair or when a player has already won the initial coin tosses or, equivalently, starts with a lead. This thesis will first cover a proof of the arcsine distribution. Then, we explore how the distribution changes when the coin the is unfair. Finally, we will explore the distribution when one person has won the first …
Comparison Of Imputation Methods For Mixed Data Missing At Random,
2019
East Tennessee State University
Comparison Of Imputation Methods For Mixed Data Missing At Random, Kaitlyn Heidt
Electronic Theses and Dissertations
A statistician's job is to produce statistical models. When these models are precise and unbiased, we can relate them to new data appropriately. However, when data sets have missing values, assumptions to statistical methods are violated and produce biased results. The statistician's objective is to implement methods that produce unbiased and accurate results. Research in missing data is becoming popular as modern methods that produce unbiased and accurate results are emerging, such as MICE in R, a statistical software. Using real data, we compare four common imputation methods, in the MICE package in R, at different levels of missingness. The …
A Systematic Assessment Of Socio-Economic Impacts Of Prolonged Episodic Volcano Crises,
2019
East Tennessee State University
A Systematic Assessment Of Socio-Economic Impacts Of Prolonged Episodic Volcano Crises, Justin Peers
Electronic Theses and Dissertations
Uncertainty surrounding volcanic activity can lead to socio-economic crises with or without an eruption as demonstrated by the post-1978 response to unrest of Long Valley Caldera (LVC), CA. Extensive research in physical sciences provides a foundation on which to assess direct impacts of hazards, but fewer resources have been dedicated towards understanding human responses to volcanic risk. To evaluate natural hazard risk issues at LVC, a multi-hazard, mail-based, household survey was conducted to compare perceptions of volcanic, seismic, and wildfire hazards. Impacts of volcanic activity on housing prices and businesses were examined at the county-level for three volcanoes with a …
Feasibility Of Multi-Year Forecast For The Colorado River Water Supply: Time Series Modeling,
2019
Utah State University
Feasibility Of Multi-Year Forecast For The Colorado River Water Supply: Time Series Modeling, Brian Plucinski
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
The Colorado River is one of the largest resources for water in the United States, as well as being an important asset to the economy. Previous studies have shown a connection between the Great Salt Lake and the Colorado River. This study used time series analysis to build models to predict the water supply of the Colorado River ten years out. These models used data from the Colorado River in addition to Great Salt Lake water elevation. Several models suggest a decline in water supply from 2013 – 2020, before starting to increase. These predictions differ from predictions published by …
Predictive Distributions Via Filtered Historical Simulation For Financial Risk Management,
2019
Utah State University
Predictive Distributions Via Filtered Historical Simulation For Financial Risk Management, Tyson Clark
All Graduate Plan B and other Reports, Spring 1920 to Spring 2023
Filtered historical simulation with an underlying GARCH process can be used as a valuable tool in VaR analysis, as it derives risk estimates that are sensitive to the distributional properties of the historical data of the produced predictive density. I examine the applications to risk analysis that filtered historical simulation can provide, as well as an interpretation of the predictive density as a poor man’s Bayesian posterior distribution. The predictive density allows us to make associated probabilistic statements regarding the results for VaR analysis, giving greater measurement of risk and the ability to maintain the optimal level of risk per …
Advanced Statistics In Arkansas Sports Reporting,
2019
University of Arkansas, Fayetteville
Advanced Statistics In Arkansas Sports Reporting, Andrew Lee Epperson
Graduate Theses and Dissertations
This study seeks to analyze how Arkansas’ sports journalists are adapting to the recent surge in available advanced statistics that are being used by certain national news organizations. Using in-depth qualitative research that includes in-depth interviews with a number of individuals in the print, broadcast, and athletics side of sports coverage, we discover how journalists and coaches use these next-generation analytics, what they fundamentally mean for the evolution of each respective path, and why so few Arkansas reporters and writers use them at the time of this paper’s defense. We see how budgets and deadlines restrict the use of these …
Comparing Elo, Glicko, Irt, And Bayesian Irt Statistical Models For Educational And Gaming Data,
2019
University of Arkansas, Fayetteville
Comparing Elo, Glicko, Irt, And Bayesian Irt Statistical Models For Educational And Gaming Data, Breanna Morrison
Graduate Theses and Dissertations
Statistical models used for estimating skill or ability levels often vary by field, however their underlying mathematical models can be very similar. Differences in the underlying models can be due to the need to accommodate data with different underlying formats and structure. As the models from varying fields increase in complexity, their ability to be applied to different types of data may have the ability to increase. Models that are applied to educational or psychological data have advanced to accommodate a wide range of data formats, including increased estimation accuracy with sparsely populated data matrices. Conversely, the field of online …
Simulation As A Predictor In Probability,
2019
CUNY New York City College of Technology
Simulation As A Predictor In Probability, Xiaona Zhou
Publications and Research
In this study, we simulate bivariate normal data. We gain intuition about the bivariate normal distribution by comparing the generated data to the associated bivariate normal density surface. We also get results about covariance and correlation. We will use tools from linear algebra to discuss transformations of random normal vectors, and the use of contours.
Ergodicity For The 3d Stochastic Navier-Stokes Equations Perturbed By Lévy Noise,
2019
Indian Institute of Technology Roorkee-IIT Roorkee
Ergodicity For The 3d Stochastic Navier-Stokes Equations Perturbed By Lévy Noise, Manil T. Mohan, K. Sakthivel, Sivaguru S. Sritharan
Faculty Publications
In this work we construct a Markov family of martingale solutions for 3D stochastic Navier–Stokes equations (SNSE) perturbed by Lévy noise with periodic boundary conditions. Using the Kolmogorov equations of integrodifferential type associated with the SNSE perturbed by Lévy noise, we construct a transition semigroup and establish the existence of a unique invariant measure. We also show that it is ergodic and strongly mixing.
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Raman And Surface Enhanced Raman Spectroscopy For Forensic Analysis: Case Studies On The Identification Of Illicit Substances And Artist Pigments,
2019
CUNY Graduate Center
Raman And Surface Enhanced Raman Spectroscopy For Forensic Analysis: Case Studies On The Identification Of Illicit Substances And Artist Pigments, Abed Haddad
Dissertations, Theses, and Capstone Projects
Raman spectroscopy is an effective tool for detecting trace amounts of material by fingerprint-like vibrational spectra. At times, the weak intensity of Raman scattering can make it difficult to distinguish trace materials. This shortcoming is addressed by surface‐enhanced Raman spectroscopy (SERS), which produces strong signal enhancements when target compounds are near metal nanoparticles. For the first part of this thesis, the identification of fentanyl and carfentanil, main culprits in the opioid epidemic, was done using normal Raman and the SERS spectroscopy. As an aid in the assignment of the spectral lines, a computational model was built using Density Functional Theory …
Is Animal-Based Biomedical Research Being Used In Its Original Context?,
2019
University of Lisbon, Portugal
Is Animal-Based Biomedical Research Being Used In Its Original Context?, Constança Carvalho, Daniel Alves, Andrew Knight, Luís Vicente
Validation of Animal Experimentation Collection
No abstract provided.
Critically Evaluating Animal Research,
2019
University of Winchester
Critically Evaluating Animal Research, Andrew Knight
Validation of Animal Experimentation Collection
No abstract provided.
Deep Learning, Medical Physics And Cargo Cult Science.,
2019
University of San Francisco
Deep Learning, Medical Physics And Cargo Cult Science., Miguel Romero Phd, Gilmer Valdes Phd, Timothy Solberg Phd, Yannet Interian Phd
Creative Activity and Research Day - CARD
Deep learning algorithms have become widely popular, with considerable success in fields where datasets have hundreds of thousands or million points. As deep learning is increasingly applied to the fields of medical physics and radiation oncology, a reasonable question follows: are these techniques the best approach, given the unique conditions in our field? In this study, we investigate the dependence of dataset size on the performance of deep learning algorithms compared with more traditional radiomics-based methods.
Deep Neural Network Architectures For Music Genre Classification,
2019
University of San Francisco
Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan
Creative Activity and Research Day - CARD
With the recent advancements in technology, many tasks in fields such as computer vision, natural language processing, and signal processing have been solved using deep learning architectures. In the audio domain, these architectures have been used to learn musical features of songs to predict: moods, genres, and instruments. In the case of genre classification, deep learning models were applied to popular datasets--which are explicitly chosen to represent their genres--and achieved state-of-the-art results. However, these results have not been reproduced on less refined datasets. To this end, we introduce an un-curated dataset which contains genre labels and 30-second audio previews for …
Variance Heterogeneity In Psychological Research: A Monte Carlo Study Of The Consequences For Meta-Analysis,
2019
St. John Fisher University
Variance Heterogeneity In Psychological Research: A Monte Carlo Study Of The Consequences For Meta-Analysis, Bruce E. Blaine
Statistics Faculty/Staff Publications
Variance heterogeneity is common in psychological research. Surveys of psychological research show that variance ratios (VRs) in two-group studies average around 2.5, with a substantial minority of studies having much higher VRs. Research has established that variance heterogeneity disturbs Type I error rates of parametric tests in primary research. Fixed-effects meta-analysis is a common statistical method in psychology for synthesizing primary research, and plays an important role in cumulative science and evidence-based practice. Little is known about the consequences of variance heterogeneity for meta-analytic estimates. The present research reports a Monte Carlo study in which the results of k = …
A Gene-Based Recessive Diplotype Exome Scan Discovers Fgf6, A Novel Hepcidin-Regulating Iron-Metabolism Gene,
2019
Marshfield Clinic Research Institute
A Gene-Based Recessive Diplotype Exome Scan Discovers Fgf6, A Novel Hepcidin-Regulating Iron-Metabolism Gene, Shicheng Guo, Shuai Jiang, Narendranath Epperla, Yanyun Ma, Mehdi Maadooliat, Zhan Ye, Brent Olson, Minghua Wang, Terrie Kitchner, Jeffrey Joyce, Peng An, Fudi Wang, Robert Strenn, Joseph J. Mazza, Jennifer K. Meece, Wenyu Wu, Li Jin, Judith A. Smith, Jiucan Wang, Steven J. Schrodi
Mathematical and Statistical Science Faculty Research and Publications
Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor–encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. …
The Andersen Likelihood Ratio Test With A Random Split Criterion Lacks Power,
2019
University College of Teacher Education Styria
The Andersen Likelihood Ratio Test With A Random Split Criterion Lacks Power, Georg Krammer
Journal of Modern Applied Statistical Methods
The Andersen LRT uses sample characteristics as split criteria to evaluate Rasch model fit, or theory driven hypothesis testing for a test. The power and Type I error of a random split criterion was evaluated with a simulation study. Results consistently show a random split criterion lacks power.
Deep Embedding Kernel,
2019
Kennesaw State University
Deep Embedding Kernel, Linh Le
Doctor of Data Science and Analytics Dissertations
Kernel methods and deep learning are two major branches of machine learning that have achieved numerous successes in both analytics and artificial intelligence. While having their own unique characteristics, both branches work through mapping data to a feature space that is supposedly more favorable towards the given task. This dissertation addresses the strengths and weaknesses of each mapping method through combining them and forming a family of novel deep architectures that center around the Deep Embedding Kernel (DEK). In short, DEK is a realization of a kernel function through a newly deep architecture. The mapping in DEK is both implicit …
Weighted Version Of Generalized Inverse Weibull Distribution,
2019
University of Kashmir, Srinagar, India
Weighted Version Of Generalized Inverse Weibull Distribution, Sofi Mudiasir, S. P. Ahmad
Journal of Modern Applied Statistical Methods
Weighted distributions are used in many fields, such as medicine, ecology, and reliability. A weighted version of the generalized inverse Weibull distribution, known as weighted generalized inverse Weibull distribution (WGIWD), is proposed. Basic properties including mode, moments, moment generating function, skewness, kurtosis, and Shannon’s entropy are studied. The usefulness of the new model was demonstrated by applying it to a real-life data set. The WGIWD fits better than its submodels, such as length biased generalized inverse Weibull (LGIW), generalized inverse Weibull (GIW), inverse Weibull (IW) and inverse exponential (IE) distributions.
