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A Robust Nonparametric Measure Of Effect Size Based On An Analog Of Cohen's D, Plus Inferences About The Median Of The Typical Difference, Rand Wilcox 2019 University of Southern California

A Robust Nonparametric Measure Of Effect Size Based On An Analog Of Cohen's D, Plus Inferences About The Median Of The Typical Difference, Rand Wilcox

Journal of Modern Applied Statistical Methods

The paper describes a nonparametric analog of Cohen's d, Q. It is established that a confidence interval for Q can be computed via a method for computing a confidence interval for the median of D = X1X2, which in turn is related to making inferences about P(X1 < X2).


Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox 2019 University of Southern California

Robust Ancova, Curvature, And The Curse Of Dimensionality, Rand Wilcox

Journal of Modern Applied Statistical Methods

There is a substantial collection of robust analysis of covariance (ANCOVA) methods that effectively deals with non-normality, unequal population slope parameters, outliers, and heteroscedasticity. Some are based on the usual linear model and others are based on smoothers (nonparametric regression estimators). However, extant results are limited to one or two covariates. A minor goal here is to extend a recently-proposed method, based on the usual linear model, to situations where there are up to six covariates. The usual linear model might provide a poor approximation of the true regression surface. The main goal is to suggest a method, based on ...


Logistic Regression: An Inferential Method For Identifying The Best Predictors, Rand Wilcox 2019 University of Southern California

Logistic Regression: An Inferential Method For Identifying The Best Predictors, Rand Wilcox

Journal of Modern Applied Statistical Methods

When dealing with a logistic regression model, there is a simple method for estimating the strength of the association between the jth covariate and the dependent variable when all covariates are entered into the model. There is the issue of determining whether the jth independent variable has a stronger or weaker association than the kth independent variable. This note describes a method for dealing with this issue that was found to perform reasonably well in simulations.


Should We Give Up On Causality?, Tom Knapp 2019 The Ohio State University

Should We Give Up On Causality?, Tom Knapp

Journal of Modern Applied Statistical Methods

No abstract provided.


A Strategy For Using Bias And Rmse As Outcomes In Monte Carlo Studies In Statistics, Michael Harwell 2019 University of Minnesota - Twin Cities

A Strategy For Using Bias And Rmse As Outcomes In Monte Carlo Studies In Statistics, Michael Harwell

Journal of Modern Applied Statistical Methods

To help ensure important patterns of bias and accuracy are detected in Monte Carlo studies in statistics this paper proposes conditioning bias and root mean square error (RMSE) measures on estimated Type I and Type II error rates. A small Monte Carlo study is used to illustrate this argument.


Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations, Graeme Ruxton, Markus Neuhäuser 2019 University of St Andrews

Striving For Simple But Effective Advice For Comparing The Central Tendency Of Two Populations, Graeme Ruxton, Markus Neuhäuser

Journal of Modern Applied Statistical Methods

Nguyen et al. (2016) offered advice to researchers in the commonly-encountered situation where they are interested in testing for a difference in central tendency between two populations. Their data and the available literature support very simple advice that strikes the best balance between ease of implementation, power and reliability. Specifically, apply Satterthwaite’s test, with preliminary ranking of the data if a strong deviation from normality is expected, or is suggested by visual inspection of the data. This simple guideline will serve well except when dealing with small samples of discrete data, when more sophisticated treatment may be required.


Data Analytics Pipeline For Rna Structure Analysis Via Shape, Quinn Nelson 2019 University of Nebraska at Omaha

Data Analytics Pipeline For Rna Structure Analysis Via Shape, Quinn Nelson

Student Research and Creative Activity Fair

Coxsackievirus B3 (CVB3) is a cardiovirulent enterovirus from the family Picornaviridae. The RNA genome houses an internal ribosome entry site (IRES) in the 5’ untranslated region (5’UTR) that enables cap-independent translation. Ample evidence suggests that the structure of the 5’UTR is a critical element for virulence. We probe RNA structure in solution using base-specific modifying agents such as dimethyl sulfate as well as backbone targeting agents such as N-methylisatoic anhydride used in Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension (SHAPE). We have developed a pipeline that merges and evaluates base-specific and SHAPE data together with statistical analyses ...


Sustainable Energy Governance In South Tyrol (Italy): A Probabilistic Bipartite Network Model, Jessica Belest, Laura Secco, Elena Pisani, Alberto Caimo 2019 EURAC Research, Italy

Sustainable Energy Governance In South Tyrol (Italy): A Probabilistic Bipartite Network Model, Jessica Belest, Laura Secco, Elena Pisani, Alberto Caimo

Articles

At the national scale, almost all of the European countries have already achieved energy transition targets, while at the regional and local scales, there is still some potential to further push sustainable energy transitions. Regions and localities have the support of political, social, and economic actors who make decisions for meeting existing social, environmental and economic needs recognising local specificities.

These actors compose the sustainable energy governance that is fundamental to effectively plan and manage energy resources. In collaborative relationships, these actors share, save, and protect several kinds of resources, thereby making energy transitions deeper and more effective.

This research ...


Multi-Linear Algebraic Eigendecompositions And Their Application In Data Science, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr. 2019 SDSMT

Multi-Linear Algebraic Eigendecompositions And Their Application In Data Science, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.

SDSU Data Science Symposium

Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal ...


Predicting Unplanned Medical Visits Among Patients With Diabetes Using Machine Learning, Arielle Selya, Eric L. Johnson 2019 Sanford Health

Predicting Unplanned Medical Visits Among Patients With Diabetes Using Machine Learning, Arielle Selya, Eric L. Johnson

SDSU Data Science Symposium

Diabetes poses a variety of medical complications to patients, resulting in a high rate of unplanned medical visits, which are costly to patients and healthcare providers alike. However, unplanned medical visits by their nature are very difficult to predict. The current project draws upon electronic health records (EMR’s) of adult patients with diabetes who received care at Sanford Health between 2014 and 2017. Various machine learning methods were used to predict which patients have had an unplanned medical visit based on a variety of EMR variables (age, BMI, blood pressure, # of prescriptions, # of diagnoses on problem list, A1C, HDL ...


Level Crossing Simulation Of A Queueing Model, Zhanxuan Ding 2019 University of Windsor

Level Crossing Simulation Of A Queueing Model, Zhanxuan Ding

Major Papers

Simulation of the level crossing method will be used to find approximations of the distribution of the workload for several queueing models. In particular, three different type of queueing models, with different methods of handling workload bound thresholds, will be considered. Simulation applied to workload bound thresholds is new work.


Pedestrian Safety -- Fundamental To A Walkable City, Joshua Herrera, Patrick McDevitt, Preeti Swaminathan, Raghuram Srinivas 2019 Southern Methodist University

Pedestrian Safety -- Fundamental To A Walkable City, Joshua Herrera, Patrick Mcdevitt, Preeti Swaminathan, Raghuram Srinivas

SMU Data Science Review

In this paper, we present a method to identify urban areas with a higher likelihood of pedestrian safety related events. Pedestrian safety related events are pedestrian-vehicle interactions that result in fatalities, injuries, accidents without injury, or near--misses between pedestrians and vehicles. To develop a solution to this problem of identifying likely event locations, we assemble data, primarily from the City of Cincinnati and Hamilton County, that include safety reports from a five year period, geographic information for these events, citizen survey of pedestrian reported concerns, non-emergency requests for service for any cause in the city, property values and public transportation ...


Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater 2019 Southern Methodist University

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide ...


Numeracy And Social Justice: A Wide, Deep, And Longstanding Intersection, Kira Hamman, Victor Piercey, Samuel L. Tunstall 2019 Pennsylvania State University, Mont Alto

Numeracy And Social Justice: A Wide, Deep, And Longstanding Intersection, Kira Hamman, Victor Piercey, Samuel L. Tunstall

Numeracy

We discuss the connection between the numeracy and social justice movements both in historical context and in its modern incarnation. The intersection between numeracy and social justice encompasses a wide variety of disciplines and quantitative topics, but within that variety there are important commonalities. We examine the importance of sound quantitative measures for understanding social issues and the necessity of interdisciplinary collaboration in this work. Particular reference is made to the papers in the first part of the Numeracy special collection on social justice, which appear in this issue.


Comparative Analysis Of Students’ Performance Between Online And On Campus In An Introductory Statistics Course, Kendal McDonald 2019 Georgia College and State University

Comparative Analysis Of Students’ Performance Between Online And On Campus In An Introductory Statistics Course, Kendal Mcdonald

The Corinthian

In this research, we compare students’ performance in an online and on-campus introductory statistics and probability course at Georgia College. MyStatLab is the learning management system used in both the online and on-campus courses for homework and quizzes. The online data is produced by five summer courses between Summer 2014 to Summer 2017 and the on-campus data is produced from nine on-campus courses from Spring 2014, Spring 2016, and Spring 2017. For homework, the research compares the scores made between online and on-campus. For quizzes, we test if there is a difference between the scores and the number of attempts ...


Population Viability And Connectivity Of The Federally Threatened Eastern Indigo Snake In Central Peninsular Florida, Javan Bauder 2019 University of Massachusetts Amherst

Population Viability And Connectivity Of The Federally Threatened Eastern Indigo Snake In Central Peninsular Florida, Javan Bauder

Doctoral Dissertations

Understanding the factors influencing the likelihood of persistence of real-world populations requires both an accurate understanding of the traits and behaviors of individuals within those populations (e.g., movement, habitat selection, survival, fecundity, dispersal) but also an understanding of how those traits and behaviors are influenced by landscape features. The federally threatened eastern indigo snake (EIS, Drymarchon couperi) has declined throughout its range primarily due to anthropogenically-induced habitat loss and fragmentation making spatially-explicit assessments of population viability and connectivity essential for understanding its current status and directing future conservation efforts.

The primary goal of my dissertation was to understand how ...


Automatic 13C Chemical Shift Reference Correction Of Protein Nmr Spectral Data Using Data Mining And Bayesian Statistical Modeling, Xi Chen 2019 University of kencutky

Automatic 13C Chemical Shift Reference Correction Of Protein Nmr Spectral Data Using Data Mining And Bayesian Statistical Modeling, Xi Chen

Theses and Dissertations--Molecular and Cellular Biochemistry

Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially of biomacromolecules such as proteins. However, due to the intrinsic properties of NMR experiments, results from the NMR instruments require a refencing step before the down-the-line analysis. Poor chemical shift referencing, especially for 13C in protein Nuclear Magnetic Resonance (NMR) experiments, fundamentally limits and even prevents effective study of biomacromolecules via NMR. There is no available method that can rereference carbon chemical shifts from protein NMR without secondary experimental information such as structure or resonance assignment.

To solve this problem, we ...


A Latent Spatial Piecewise Exponential Model For Interval-Censored Disease Surveillance Data With Time-Varying Covariates And Misclassification, Yaxuan Sun, Chong Wang, William Q. Meeker, Max Morris, Marisa L. Rotolo, Jeffery Zimmerman 2019 Iowa State University

A Latent Spatial Piecewise Exponential Model For Interval-Censored Disease Surveillance Data With Time-Varying Covariates And Misclassification, Yaxuan Sun, Chong Wang, William Q. Meeker, Max Morris, Marisa L. Rotolo, Jeffery Zimmerman

Veterinary Diagnostic and Production Animal Medicine Publications

Understanding the dynamics of disease spread is critical to achieving effective animal disease surveillance. A major challenge in modeling disease spread is the fact that the true disease status cannot be known with certainty due to the imperfect diagnostic sensitivity and specificity of the tests used to generate the disease surveillance data. Other challenges in modeling such data include interval censoring, relating disease spread to distance between units, and incorporating time-varying covariates, which are the unobserved disease statuses. We propose a latent spatial piecewise exponential model (PEX) with misclassification of events to address the challenges in modeling such disease surveillance ...


Application Of Bayesian Network To Total Points In Nba Games, Sarah M. Ryan, Enrique Alameda-Basora 2019 Iowa State University

Application Of Bayesian Network To Total Points In Nba Games, Sarah M. Ryan, Enrique Alameda-Basora

Industrial and Manufacturing Systems Engineering Conference Proceedings and Posters

Bettors on National Basketball Association (NBA) games commonly place wagers concerning the result of a game at time points during that game. We focus on the Totals (Over/Under) bet. Although many forecasting models have been built to predict the total number of points scored in an NBA game, they fail to provide bettors engaged in live-betting with predictions that are based on the game currently being played. We construct an Expert Bayesian Network to sequentially, as the game progresses, update the probability that the total score will exceed that set by the oddsmakers, and then use this probability to ...


Forecasting Prospective Financial Health In The Context Of Postsecondary School And Major Type, Nicholas Judd 2019 University of Colorado, Boulder

Forecasting Prospective Financial Health In The Context Of Postsecondary School And Major Type, Nicholas Judd

Undergraduate Honors Theses

Abstract

This paper investigates the relationship between collegiate school and major type and prospective graduate financial health. The model relies on data from the National Survey of College Graduates 2013 and 2015 cohorts. Using a method of backwards induction combined with probabilistic expectation, the model seeks to explain how initial school type (as measured by Carnegie Classification) and major type predict the expected years of financial distress and present-discounted net future earnings for undergraduates. Findings suggest that colleges characterized by higher selectivity and funding forecast better overall financial health through reduced years of distress and higher earnings value. This relationship ...


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