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Stochastic Modeling Of Ovarian Follicle Growth In Adult Female Rats, Zhaozhi Li 2020 Illinois State University

Stochastic Modeling Of Ovarian Follicle Growth In Adult Female Rats, Zhaozhi Li

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.


Applying The Data: Predictive Analytics In Sport, Anthony Teeter, Margo Bergman 2020 University of Washington, Tacoma

Applying The Data: Predictive Analytics In Sport, Anthony Teeter, Margo Bergman

Access*: Interdisciplinary Journal of Student Research and Scholarship

The history of wagering predictions and their impact on wide reaching disciplines such as statistics and economics dates to at least the 1700’s, if not before. Predicting the outcomes of sports is a multibillion-dollar business that capitalizes on these tools but is in constant development with the addition of big data analytics methods. Sportsline.com, a popular website for fantasy sports leagues, provides odds predictions in multiple sports, produces proprietary computer models of both winning and losing teams, and provides specific point estimates. To test likely candidates for inclusion in these prediction algorithms, the authors developed a computer model ...


Interval Estimation Of Proportion Of Second-Level Variance In Multi-Level Modeling, Steven Svoboda 2020 University of Nebraska-Lincoln

Interval Estimation Of Proportion Of Second-Level Variance In Multi-Level Modeling, Steven Svoboda

The Nebraska Educator: A Student-Led Journal

Physical, behavioral and psychological research questions often relate to hierarchical data systems. Examples of hierarchical data systems include repeated measures of students nested within classrooms, nested within schools and employees nested within supervisors, nested within organizations. Applied researchers studying hierarchical data structures should have an estimate of the intraclass correlation coefficient (ICC) for every nested level in their analyses because ignoring even relatively small amounts of interdependence is known to inflate Type I error rate in single-level models. Traditionally, researchers rely upon the ICC as a point estimate of the amount of interdependency in their data. Recent methods utilizing an ...


A Management Strategy Evaluation Of The Impacts Of Interspecific Competition And Recreational Fishery Dynamics On Vermilion Snapper (Rhomboplites Aurorubens) In The Gulf Of Mexico, Megumi C. Oshima 2020 University of Southern Mississippi

A Management Strategy Evaluation Of The Impacts Of Interspecific Competition And Recreational Fishery Dynamics On Vermilion Snapper (Rhomboplites Aurorubens) In The Gulf Of Mexico, Megumi C. Oshima

Dissertations

In the Gulf of Mexico (GOM), Vermilion Snapper (Rhomboplites auroruben), are believed to compete with Red Snapper directly for prey and habitat. The two species share similar diets and have significant spatial overlap in the Gulf. Red Snapper are thought to be the dominate competitor, forcing Vermilion Snapper to feed on less nutritious prey when local resources are depleted. In addition to ecological pressures, GOM Vermilion Snapper support substantial commercial and recreational fisheries. Over the past decade, recreational landings have steadily increased, reaching a historical high in 2018. One cause may be stricter regulations for similar target species such as ...


Predicting County-Scale Maize Yields With Publicly Available Data, Zehui Jiang, Chao Liu, Baskar Ganapathysubramanian, Dermot J. Hayes, Soumik Sarkar 2020 Iowa State University

Predicting County-Scale Maize Yields With Publicly Available Data, Zehui Jiang, Chao Liu, Baskar Ganapathysubramanian, Dermot J. Hayes, Soumik Sarkar

Economics Publications

Maize (corn) is the dominant grain grown in the world. Total maize production in 2018 equaled 1.12 billion tons. Maize is used primarily as an animal feed in the production of eggs, dairy, pork and chicken. The US produces 32% of the world’s maize followed by China at 22% and Brazil at 9% (https://apps.fas.usda.gov/psdonline/app/index.html#/app/home). Accurate national-scale corn yield prediction critically impacts mercantile markets through providing essential information about expected production prior to harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in ...


Fecal And Ruminal Microbiome Components Associated With Methane Emission In Beef Cattle, Bruno G. N. Andrade, Haithem Afli, Flavia A. Bressani, Rafael R. C. Cuadrat, Priscila S. N. de Oliveira, Gerson B. Mourão, Luiz L. Coutinho, James M. Reecy, James E. Koltes, Marcela Maria de Souza, Adhemar Zerlotini Neto, Sérgio Raposo de Medeiros, Alexandre Berndt, Julio C. P. Palhares, Luciana C. A. Regitano 2020 Embrapa Pecuaria Sudeste

Fecal And Ruminal Microbiome Components Associated With Methane Emission In Beef Cattle, Bruno G. N. Andrade, Haithem Afli, Flavia A. Bressani, Rafael R. C. Cuadrat, Priscila S. N. De Oliveira, Gerson B. Mourão, Luiz L. Coutinho, James M. Reecy, James E. Koltes, Marcela Maria De Souza, Adhemar Zerlotini Neto, Sérgio Raposo De Medeiros, Alexandre Berndt, Julio C. P. Palhares, Luciana C. A. Regitano

Animal Science Publications

Background: The impact of extreme changes in weather patterns in the economy and humanity welfare are some of the biggest challenges that our civilization is facing. From the anthropogenic activities that contribute to climate change, reducing the impact of farming activities is a priority, since its responsible for up to 18% of greenhouse gases linked to such activities. To this end, we tested if the ruminal and fecal microbiomes components of 52 Brazilian Nelore bulls, belonging to two experimental groups based on the feed intervention, conventional (A) and byproducts based diet (B), could be used as biomarkers for methane (CH ...


Assessing Invasiveness Of Subsolid Lung Adenocarcinomas With Combined Attenuation And Geometric Feature Models, Constance de Margerie-Mellon, Ritu R. Gill, Pascal Salazar, Anastasia Oikonomou, Elsie T. Nguyen, Benedikt H. Heidinger, Mayra A. Medina, Paul A. VanderLaan, Alexander A. Bankier 2020 Harvard Medical School

Assessing Invasiveness Of Subsolid Lung Adenocarcinomas With Combined Attenuation And Geometric Feature Models, Constance De Margerie-Mellon, Ritu R. Gill, Pascal Salazar, Anastasia Oikonomou, Elsie T. Nguyen, Benedikt H. Heidinger, Mayra A. Medina, Paul A. Vanderlaan, Alexander A. Bankier

Open Access Publications by UMMS Authors

The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume ...


Semiparametric Imputation Using Conditional Gaussian Mixture Models Under Item Nonresponse, Danhyang Lee, Jae Kwang Kim 2020 University of Alabama

Semiparametric Imputation Using Conditional Gaussian Mixture Models Under Item Nonresponse, Danhyang Lee, Jae Kwang Kim

Statistics Publications

Imputation is a popular technique for handling item nonresponse in survey sampling. Parametric imputation is based on a parametric model for imputation and is less robust against the failure of the imputation model. Nonparametric imputation is fully robust but is not applicable when the dimension of covariates is large due to the curse of dimensionality. Semiparametric imputation is another robust imputation based on a flexible model where the number of model parameters can increase with the sample size. In this paper, we propose another semiparametric imputation based on a more flexible model assumption than the Gaussian mixture model. In the ...


A Differential Geometry-Based Machine Learning Algorithm For The Brain Age Problem, Justin Asher, Khoa Tan Dang, Maxwell Masters 2020 Purdue University Fort Wayne

A Differential Geometry-Based Machine Learning Algorithm For The Brain Age Problem, Justin Asher, Khoa Tan Dang, Maxwell Masters

The Journal of Purdue Undergraduate Research

No abstract provided.


Predicting Postoperative Delirium Risk For Intracranial Surgery: A Statistical Machine Learning Approach, Juliet Aygun, Alaina Bartfeld, Sahana Rayan 2020 Purdue University

Predicting Postoperative Delirium Risk For Intracranial Surgery: A Statistical Machine Learning Approach, Juliet Aygun, Alaina Bartfeld, Sahana Rayan

The Journal of Purdue Undergraduate Research

No abstract provided.


A Geochemical And Statistical Investigation Of The Big Four Springs Region In Southern Missouri, Jordan Jasso Vega 2020 Missouri State University

A Geochemical And Statistical Investigation Of The Big Four Springs Region In Southern Missouri, Jordan Jasso Vega

MSU Graduate Theses

The Big Four Springs region hosts four major first-order magnitude springs in southern Missouri and northern Arkansas. These springs are Big Spring (Carter County, MO), Greer Spring (Oregon County, MO), Mammoth Spring (Fulton County, AR), and Hodgson Mill Spring (Ozark County, MO). Based on historic dye traces and hydrogeological investigations, these springs drain an area of approximately 1500 square miles and collectively discharge an average of 780 million gallons of water per day. The rocks from youngest to oldest that are found in Big Four Springs region are the Cotter and Jefferson City Dolomite (Ordovician), Roubidoux Formation (Ordovician), Gasconade Dolomite ...


D-Vine Pair-Copula Models For Longitudinal Binary Data, Huihui Lin 2020 Old Dominion University

D-Vine Pair-Copula Models For Longitudinal Binary Data, Huihui Lin

Mathematics & Statistics Theses & Dissertations

Dependent longitudinal binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. A popular method for analyzing such data is the multivariate probit (MP) model. The motivation for this dissertation stems from the fact that the MP model fails even the binary correlations are within the feasible range. The reason being the underlying correlation matrix of the latent variables in the MP model may not be positive definite. In this dissertation, we study alternatives that are based on D-vine pair-copula models. We consider both the serial dependence modeled by the first order autoregressive (AR(1 ...


Lectures On Mathematical Computing With Python, Jay Gopalakrishnan 2020 Portland State University

Lectures On Mathematical Computing With Python, Jay Gopalakrishnan

PDXOpen: Open Educational Resources

This open resource is a collection of class activities for use in undergraduate courses aimed at teaching mathematical computing, and computational thinking in general, using the python programming language. It was developed for a second-year course (MTH 271) revamped for a new undergraduate program in data science at Portland State University. The activities are designed to guide students' use of python modules effectively for scientific computation, data analysis, and visualization.

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Statistical Methodology To Establish A Benchmark For Evaluating Antimicrobial Resistance Genes Through Real Time Pcr Assay, Enakshy Dutta 2020 University of Nebraska - Lincoln

Statistical Methodology To Establish A Benchmark For Evaluating Antimicrobial Resistance Genes Through Real Time Pcr Assay, Enakshy Dutta

Dissertations and Theses in Statistics

Novel diagnostic tests are usually compared with gold standard tests for evaluating diagnostic accuracy. For assessing antimicrobial resistance (AMR) to bovine respiratory disease (BRD) pathogens, phenotypic broth microdilution method is used as gold standard (GS). The objective of the thesis is to evaluate the optimal cycle threshold (Ct) generated by real-time polymerase chain reaction (rtPCR) to genes that confer resistance that will translate to the phenotypic classification of AMR. Data from two different methodologies are assessed to identify Ct that will discriminate between resistance (R) and susceptibility (S). First, the receiver operating characteristic (ROC) curve was used to determine the ...


An Ncaa Football Bowl Subdivision Production Function, Stacey L. Brook 2020 Driehaus College of Business, DePaul University

An Ncaa Football Bowl Subdivision Production Function, Stacey L. Brook

Journal of Emerging Sport Studies

Each year pundits across the NCAA football landscape debate the validity of various NCAA football teams’ relative worthiness to play for the national championship. Given this debate seems to revolve around which team is the best in terms of total team production, I have developed and statistically estimated a complex invasion NCAA football bowl subdivision production function measuring NCAA football team productivity covering the 2008 to 2017 seasons. The model estimates both points scored and points surrendered for each team during this time period and then is combined to determine each team’s overall productivity. Finally, as an application of ...


Latent Class Models For At-Risk Populations, Shuaimin Kang 2020 University of Massachusetts Amherst

Latent Class Models For At-Risk Populations, Shuaimin Kang

Doctoral Dissertations

Clustering Network Tree Data From Respondent-Driven Sampling With Application to Opioid Users in New York City

There is great interest in finding meaningful subgroups of attributed network data. There are many available methods for clustering complete network. Unfortunately, much network data is collected through sampling, and therefore incomplete. Respondent-driven sampling (RDS) is a widely used method for sampling hard-to-reach human populations based on tracing links in the underlying unobserved social network. The resulting data therefore have tree structure representing a sub-sample of the network, along with many nodal attributes. In this paper, we introduce an approach to adjust mixture models ...


Analytical Scuc/Sced Optimization Formulation For Ames V5.0, Leigh Tesfatsion, Swathi Battula 2020 Iowa State University

Analytical Scuc/Sced Optimization Formulation For Ames V5.0, Leigh Tesfatsion, Swathi Battula

Economics Working Papers

U.S. centrally-managed wholesale power markets currently rely on Security-Constrained Unit Commitment (SCUC) and Security Constrained Economic Dispatch (SCED) optimizations to determine unit commitments, reserve, and scheduled dispatch levels for generating units during future operating periods. AMES V5.0 is an open source Java/Python platform that implements a combined SCUC/SCED optimization capturing salient features of these actual market SCUC/SCED optimizations. This report provides extensive documentation for the analytical formulation of the AMES V5.0 SCUC/SCED optimization.


Causal Inference And Prediction On Observational Data With Survival Outcomes, Xiaofei Chen 2020 Southern Methodist University / UT Southwestern Medical Center

Causal Inference And Prediction On Observational Data With Survival Outcomes, Xiaofei Chen

Statistical Science Theses and Dissertations

Infants with hypoplastic left heart syndrome require an initial Norwood operation, followed some months later by a stage 2 palliation (S2P). The timing of S2P is critical for the operation’s success and the infant’s survival, but the optimal timing, if one exists, is unknown. We attempt to estimate the optimal timing of S2P by analyzing data from the Single Ventricle Reconstruction Trial (SVRT), which randomized patients between two different types of Norwood procedure. In the SVRT, the timing of the S2P was chosen by the medical team; thus with respect to this exposure, the trial constitutes an observational ...


Structural Analysis Of The Multifunctional Spoiie Regulatory Protein Of Clostridioides Difficile., Blythe Emily Bunkers 2020 University of Arkansas, Fayetteville

Structural Analysis Of The Multifunctional Spoiie Regulatory Protein Of Clostridioides Difficile., Blythe Emily Bunkers

Theses and Dissertations

Clostridioides (formally Clostridium) difficile is a medically relevant pathogen pertinent to infectious disease research. C. difficile is distinctly known for its ability to produce two toxins, enterotoxin A and cytotoxin B, and the propensity to colonize the mammalian gastrointestinal tract. It is known that metabolism is tightly correlated with sporulation in endospore producers such as C. difficile, but an interesting and novel regulatory relationship found by the Ivey lab has yet to be understood. The relationship explored in this study is observed between the sporulation factor, SpoIIE, which represses expression of an ABC peptide transporter, app. In this study, two ...


Working Children On Java Island 2017, Yuniarti 2020 Syracuse University

Working Children On Java Island 2017, Yuniarti

English Language Institute

Children's wellbeing has currently become a global concern as many of them are engaged in the labor force. A small area estimation (SAE) technique, EBLUP under Fey Herriot model, is employed to reveal their number in regencies of Java Island. Statistics have been disaggregated by geographical location (urban/rural) and gender. These statistics are required by the government as the basis for policy making.


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