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Oscillation Of Nonlinear Third-Order Difference Equations With Mixed Neutral Terms, Jehad Alzabut, Martin Bohner, Said R. Grace 2021 Missouri University of Science and Technology

Oscillation Of Nonlinear Third-Order Difference Equations With Mixed Neutral Terms, Jehad Alzabut, Martin Bohner, Said R. Grace

Mathematics and Statistics Faculty Research & Creative Works

In this paper, new oscillation results for nonlinear third-order difference equations with mixed neutral terms are established. Unlike previously used techniques, which often were based on Riccati transformation and involve limsup or liminf conditions for the oscillation, the main results are obtained by means of a new approach, which is based on a comparison technique. Our new results extend, simplify, and improve existing results in the literature. Two examples with specific values of parameters are offered.


Integrated Machine Learning Approaches To Improve Classification Performance And Feature Extraction Process For Eeg Dataset, Mohammad Masum 2021 Kennesaw State University

Integrated Machine Learning Approaches To Improve Classification Performance And Feature Extraction Process For Eeg Dataset, Mohammad Masum

Analytics and Data Science Dissertations

Epileptic seizure or epilepsy is a chronic neurological disorder that occurs due to brain neurons' abnormal activities and has affected approximately 50 million people worldwide. Epilepsy can affect patients’ health and lead to life-threatening emergencies. Early detection of epilepsy is highly effective in avoiding seizures by intervening in treatment. The electroencephalogram (EEG) signal, which contains valuable information of electrical activity in the brain, is a standard neuroimaging tool used by clinicians to monitor and diagnose epilepsy. Visually inspecting the EEG signal is an expensive, tedious, and error-prone practice. Moreover, the result varies with different neurophysiologists for an identical reading. Thus ...


Causal Mediation Analysis With Multiple Time-Varying Mediators, An-Shun Tai, Sheng-Hsuan Lin, Yu-Cheng Chu, Tsung Yu, Milo A. Puhan, Tyler VanderWeele 2021 National Yang Ming Chiao Tung University

Causal Mediation Analysis With Multiple Time-Varying Mediators, An-Shun Tai, Sheng-Hsuan Lin, Yu-Cheng Chu, Tsung Yu, Milo A. Puhan, Tyler Vanderweele

Harvard University Biostatistics Working Paper Series

In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose ...


Physical Activity Barriers And Assets In Rural Appalachian Kentucky: A Mixed-Methods Study, Natalie Jones, Deirdre Dlugonski, Rachel Gillespie, Emily M. DeWitt, Joann Lianekhammy, Stacey A. Slone, Kathryn M. Cardarelli 2021 University of Kentucky

Physical Activity Barriers And Assets In Rural Appalachian Kentucky: A Mixed-Methods Study, Natalie Jones, Deirdre Dlugonski, Rachel Gillespie, Emily M. Dewitt, Joann Lianekhammy, Stacey A. Slone, Kathryn M. Cardarelli

Sports Medicine Research Institute Faculty Publications

Obesity is an increasing public health concern in the U.S. and a contributor to chronic illness, with trends revealing a rise in adult obesity and chronic disease rates among the most vulnerable and disadvantaged populations, including those in rural communities. A mixed-methods approach was used to examine perspectives on perceived physical activity barriers, resources, and level of community support. Researchers utilized the socioecological model to examine the multiple domains that support physical activity in rural Appalachia. The present study focuses on baseline data, including a cohort survey to assess physical activity, health status, and barriers to physical activity, and ...


Be Careful! That Is Probably Bullshit! Review Of Calling Bullshit: The Art Of Skepticism In A Data-Driven World By Carl T. Bergstrom And Jevin D. West, James B. Schreiber 2021 Duquesne University

Be Careful! That Is Probably Bullshit! Review Of Calling Bullshit: The Art Of Skepticism In A Data-Driven World By Carl T. Bergstrom And Jevin D. West, James B. Schreiber

Numeracy

Bergstrom, C. T., & West, J. D. 2021. Calling Bullshit: The Art of Skepticism in a Data-Driven World. NY: Random House. 336 pp. ISBN 978-0525509189

The authors provide a journey through the numerical bullshit that surrounds our daily lives. Each chapter has multiple examples of specific types of bullshit that each of us experience on any given day. Most importantly, information on how to identify bullshit and refute it are provided so that reader finishes the book with a set of skills to be a more engaged and critical interpreter of information. The writing has a quick and lively pace that ...


Identification Of Novel And Rare Variants Associated With Handgrip Strength Using Whole Genome Sequence Data From The Nhlbi Trans-Omics In Precision Medicine (Topmed) Program, Chloé Sarnowski, Han Chen, Mary L. Biggs, Sylvia Wassertheil-Smoller, Jan Bressler, Marguerite R. Irvin, Kathleen A. Ryan, David Karasik, Donna K. Arnett, L. Adrienne Cupples, David W. Fardo, Stephanie M. Gogarten, Benjamin D. Heavner, Deepti Jain, Hyun Min Kang, Charles Kooperberg, Arch G. Mainous, Braxton D. Mitchell, Alanna C. Morrison, Jeffrey R. O'Connell 2021 Boston University

Identification Of Novel And Rare Variants Associated With Handgrip Strength Using Whole Genome Sequence Data From The Nhlbi Trans-Omics In Precision Medicine (Topmed) Program, Chloé Sarnowski, Han Chen, Mary L. Biggs, Sylvia Wassertheil-Smoller, Jan Bressler, Marguerite R. Irvin, Kathleen A. Ryan, David Karasik, Donna K. Arnett, L. Adrienne Cupples, David W. Fardo, Stephanie M. Gogarten, Benjamin D. Heavner, Deepti Jain, Hyun Min Kang, Charles Kooperberg, Arch G. Mainous, Braxton D. Mitchell, Alanna C. Morrison, Jeffrey R. O'Connell

Epidemiology Faculty Publications

Handgrip strength is a widely used measure of muscle strength and a predictor of a range of morbidities including cardiovascular diseases and all-cause mortality. Previous genome-wide association studies of handgrip strength have focused on common variants primarily in persons of European descent. We aimed to identify rare and ancestry-specific genetic variants associated with handgrip strength by conducting whole-genome sequence association analyses using 13,552 participants from six studies representing diverse population groups from the Trans-Omics in Precision Medicine (TOPMed) Program. By leveraging multiple handgrip strength measures performed in study participants over time, we increased our effective sample size by 7-12 ...


Evaluating Public Masking Mandates On Covid-19 Growth Rates In U.S. States, Angus K. Wong 2021 University of Massachusetts Amherst

Evaluating Public Masking Mandates On Covid-19 Growth Rates In U.S. States, Angus K. Wong

Masters Theses

U.S. state governments have implemented numerous policies to help mitigate the spread of COVID-19. While there is strong biological evidence supporting the wearing of face masks or coverings in public spaces, the impact of public masking policies remains unclear. We aimed to evaluate how early versus delayed implementation of state-level public masking orders impacted subsequent COVID-19 growth rates. We defined “early” implementation as having a state-level mandate in place before September 1, 2020, the approximate start of the school-year. We defined COVID-19 growth rates as the relative increase in confirmed cases 7, 14, 21, 30, 45, 60-days after September ...


Model-Free Descriptive Modeling For Multivariate Categorical Data With An Ordinal Dependent Variable, Li Wang 2021 University of Massachusetts Amherst

Model-Free Descriptive Modeling For Multivariate Categorical Data With An Ordinal Dependent Variable, Li Wang

Doctoral Dissertations

In the process of statistical modeling, the descriptive modeling plays an essential role in accelerating the formulation of plausible hypotheses in the subsequent explanatory modeling and facilitating the selection of potential variables in the subsequent predictive modeling. Especially, for multivariate categorical data analysis, it is desirable to use the descriptive modeling methods for uncovering and summarizing the potential association structure among multiple categorical variables in a compact manner. However, many classical methods in this case either rely on strong assumptions for parametric models or become infeasible when the data dimension is higher. To this end, we propose a model-free method ...


Lab Exercises For Statistics Using Excel, Julia Nebia, Steven Cosares, Milena Cuellar 2021 CUNY LaGuardia Community College

Lab Exercises For Statistics Using Excel, Julia Nebia, Steven Cosares, Milena Cuellar

Open Educational Resources

This document contains the text associated with a series of computer-based lab exercises to help students apply the concepts usually included in a first course in Statistics. A compressed file has been included that contains a separate folder for each lab. In each folder is an excel spreadsheet file and an editable word document providing the instructions for students to complete the exercise. The exercises are not numbered in the folders, so you can select any subset of these exercises to assign to your students. You are free to modify the instructions in any way you see fit, e.g ...


Monty Hall Meets Game Theory, Jamie Lynn Dobson 2021 Rhode Island College

Monty Hall Meets Game Theory, Jamie Lynn Dobson

Honors Projects Overview

I explored the Monty Hall game scenario and how to calculate the chances of winning by staying or switching doors using a probability and game theory approach. I also calculated how these chances change when there are 4, 5,..., n doors.


On The Use Of Minimum Penalties In Statistical Learning, Ben Sherwood, Bradley S. Price 2021 University of Kansas

On The Use Of Minimum Penalties In Statistical Learning, Ben Sherwood, Bradley S. Price

Faculty & Staff Scholarship

Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not scale to other types of models. In this article we proposed the MinPEN framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using mild assumptions. The MinPen framework utilizes a novel penalty based on the minimum function to exploit detected relationships between responses. An iterative algorithm that ...


Statistical Modeling Of Daily Confirmed Covid-19 Cases And Deaths In Europe And United States, Zerui Zhang 2021 Marquette University

Statistical Modeling Of Daily Confirmed Covid-19 Cases And Deaths In Europe And United States, Zerui Zhang

Master's Theses (2009 -)

A novel coronavirus disease was first discovered in Wuhan, China, in December 2019. This new coronavirus named COVID-19 has rapidly spread and become a global threat affecting almost all the countries in the world. Therefore, it is important to know the trend of coronavirus disease to mitigate its effects. A good prediction model is crucial for the health care system to understand the trend of the COVID-19. This study aims to construct a good prediction model. Firstly, we detect change points of the time series data of daily confirmed cases and deaths of COVID-19 in the United States and Europe ...


Predicting Daily Confirmed Cases In Midwestern Central States In U.S. By Using Aima And Lstm, Yi zheng 2021 Marquette University

Predicting Daily Confirmed Cases In Midwestern Central States In U.S. By Using Aima And Lstm, Yi Zheng

Master's Theses (2009 -)

Covid-19 is an epidemic disease caused by SARS-Cov-2 virus, which is a type of coronavirus. This virus is highly contiguous, and the confirmed cases of this disease have increased rapidly in a short period. After one month of the first reported case, the World Health Organization (WHO) claims that the Covid-19 will become an international public health emergency. The main purpose of this thesis is to predict the daily confirmed cases of Covid-19 in the midwestern central states in the U.S, by using Autoregression Integrated Moving Average (ARIMA) model and Long Short-Term Memory network (LSTM), which is a type ...


Analysis Of Genetic Variants Associated With Levels Of Immune Modulating Proteins For Impact On Alzheimer’S Disease Risk Reveal A Potential Role For Siglec14, Benjamin C. Shaw, Yuriko Katsumata, James F. Simpson, David W. Fardo, Steven Estus 2021 University of Kentucky

Analysis Of Genetic Variants Associated With Levels Of Immune Modulating Proteins For Impact On Alzheimer’S Disease Risk Reveal A Potential Role For Siglec14, Benjamin C. Shaw, Yuriko Katsumata, James F. Simpson, David W. Fardo, Steven Estus

Biostatistics Faculty Publications

Genome-wide association studies (GWAS) have identified immune-related genes as risk factors for Alzheimer’s disease (AD), including TREM2 and CD33, frequently passing a stringent false-discovery rate. These genes either encode or signal through immunomodulatory tyrosine-phosphorylated inhibitory motifs (ITIMs) or activation motifs (ITAMs) and govern processes critical to AD pathology, such as inflammation and amyloid phagocytosis. To investigate whether additional ITIM and ITAM-containing family members may contribute to AD risk and be overlooked due to the stringent multiple testing in GWAS, we combined protein quantitative trait loci (pQTL) data from a recent plasma proteomics study with AD associations in a recent ...


Calibration-Based Estimators Using Different Distance Measures Under Two Auxiliary Variables: A Comparative Study, Piyush Kant Rai, Alka Singh, Muhammad Qasim 2021 Banaras Hindu University, Varanasi, India

Calibration-Based Estimators Using Different Distance Measures Under Two Auxiliary Variables: A Comparative Study, Piyush Kant Rai, Alka Singh, Muhammad Qasim

Journal of Modern Applied Statistical Methods

This article introduces calibration estimators under different distance measures based on two auxiliary variables in stratified sampling. The theory of the calibration estimator is presented. The calibrated weights based on different distance functions are also derived. A simulation study has been carried out to judge the performance of the proposed estimators based on the minimum relative root mean squared error criterion. A real-life data set is also used to confirm the supremacy of the proposed method.


Pareto Distribution Under Hybrid Censoring: Some Estimation, Gyan Prakash 2021 Moti Lal Nehru Medical College, Allahabad, India

Pareto Distribution Under Hybrid Censoring: Some Estimation, Gyan Prakash

Journal of Modern Applied Statistical Methods

In the present study, the Pareto model is considered as the model from which observations are to be estimated using a Bayesian approach. Properties of the Bayes estimators for the unknown parameters have studied by using different asymmetric loss functions on hybrid censoring pattern and their risks have compared. The properties of maximum likelihood estimation and approximate confidence length have also been investigated under hybrid censoring. The performances of the procedures are illustrated based on simulated data obtained under the Metropolis-Hastings algorithm and a real data set.


Tocilizumab And Covid-19: A Meta-Analysis Of 2120 Patients With Severe Disease And Implications For Clinical Trial Methodologies, Azza Sarfraz, Zouina Sarfraz, Muzna Sarfraz, Hinna Aftab, Zainab Pervaiz 2021 Aga Khan University

Tocilizumab And Covid-19: A Meta-Analysis Of 2120 Patients With Severe Disease And Implications For Clinical Trial Methodologies, Azza Sarfraz, Zouina Sarfraz, Muzna Sarfraz, Hinna Aftab, Zainab Pervaiz

Department of Paediatrics and Child Health

Background/aim: Since the outbreak of the COVID-19, numerous therapies to counteract this severe disease have emerged. The benefits of Tocilizumab for severely infected COVID-19 patients and the methodologies of ongoing clinical trials are explored.
Materials and methods: A systematic search adhering to PRISMA guidelines was conducted in PubMed, Cochrane Central, medRxiv, and bioRxiv using the following keywords: “Tocilizumab,” “Actemra,” “COVID-19.” An additional subsearch was conducted on Clinicaltrials.gov to locate ongoing tocilizumab trials.
Results: A total of 13 studies were included in the meta-analysis comprising 2120 patients. The treatment group had lower mortality compared to the control group (OR ...


Rattle Detection – An Automotive Case Study, Orla Hartley 2021 Jaguar Land Rover

Rattle Detection – An Automotive Case Study, Orla Hartley

International Conference on Lean Six Sigma

This case study showcases the use of statistical tools to develop an objective Squeak and Rattle (S&R) measurement and detection test for End Of Line (EOL) sign off in an automotive manufacturing environment. Audio Induced S&R is an unwanted vibration within the vehicle caused by the sound system, impacting on customer perception of vehicle quality. Testing for S&R in an automotive environment has a key challenge; how to robustly detect a rattle at the EOL and thus prevent plant escapes to the customer. The objective test developed used microphones and analysers in order to replace an e subjective listening test. Within the testing equipment settings, the length of the frequency sweep and the volume level of the sweep can be adapted, which in turn influences the output graph of calculated rattle. A Design of Experiment (DOE) was employed to find the optimised parameters required for these factors. The DOE ...


Lecture Notes On Modern Multivariate Statistical Learning-Version Iv, Stephen B. Vardeman 2021 Iowa State University and Analytics Iowa LLC

Lecture Notes On Modern Multivariate Statistical Learning-Version Iv, Stephen B. Vardeman

Statistics Publications

This set of notes is the most recent reorganization and update-in- progress of Modern Multivariate Statistical Learning course material de- veloped 2009-2020 over 7 offerings of PhD-level courses and 4 offerings of an MS-level course in the Iowa State University Statistics Department, a short course given in the Statistics Group at Los Alamos National Lab, and two offered through Statistical Horizons LLC. Early versions of the courses were based mostly on the topics and organization of The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman, though very substantial parts benefited from Izenman's Modern Multivariate Statis- tical Techniques, and ...


Robust Lag Weighted Lasso For Time Series Model, Tahir R. Dikheel, Alaa Q. Yaseen 2021 University of Al-Qadisiyah, Iraq

Robust Lag Weighted Lasso For Time Series Model, Tahir R. Dikheel, Alaa Q. Yaseen

Journal of Modern Applied Statistical Methods

The lag-weighted lasso was introduced to deal with lag effects when identifying the true model in time series. This method depends on weights to reflect both the coefficient size and the lag effects. However, the lag weighted lasso is not robust. To overcome this problem, we propose robust lag weighted lasso methods. Both the simulation study and the real data example show that the proposed methods outperform the other existing methods.


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