Statistics and Probability Commons^™

Pooling Strength Amongst Limited Datasets Using Hierarchical Bayesian Analysis, With Application To Pyroclastic Density Current Mobility Metrics, Sarah E. Ogburn, James Berger, Eliza S. Calder, Danilo Lopes, Abani K. Patra, E. Bruce Pitman, Regis Rutarindwa, Elaine Spiller, Robert L. Wolpert

Mathematics, Statistics and Computer Science Faculty Research and Publications

In volcanology, the sparsity of datasets for individual volcanoes is an important problem, which, in many cases, compromises our ability to make robust judgments about future volcanic hazards. In this contribution we develop a method for using hierarchical Bayesian analysis of global datasets to combine information across different volcanoes and to thereby improve our knowledge at individual volcanoes. The method is applied to the assessment of mobility metrics for pyroclastic density currents in order to better constrain input parameters and their related uncertainties for forward modeling. Mitigation of risk associated with such flows depends upon accurate forecasting of possible inundation …

Method For Determining Time-Resolved Heat Transfer Coefficient And Adiabatic Effectiveness Waveforms With Unsteady Film Cooling, James L. Rutledge, Jonathan F. Mccall

AFIT Patents

A new method for determining heat transfer coefficient (h) and adiabatic effectiveness (η) waveforms h(t) and η(t) from a single test uses a novel inverse heat transfer methodology to use surface temperature histories obtained using prior art approaches to approximate the h(t) and η(t) waveforms. The method best curve fits the data to a pair of truncated Fourier series.

Power And Sample Size Calculations For Interval-Censored Survival Analysis, Hae-Young Kim, John M. Williamson, Hung-Mo Lin

NYMC Faculty Publications

We propose a method for calculating power and sample size for studies involving interval-censored failure time data that only involves standard software required for fitting the appropriate parametric survival model. We use the framework of a longitudinal study where patients are assessed periodically for a response and the only resultant information available to the investigators is the failure window: the time between the last negative and first positive test results. The survival model is fit to an expanded data set using easily computed weights. We illustrate with a Weibull survival model and a two-group comparison. The investigator can specify a …

Does Research On Evaluation Matter? Findings From A Survey Of American Evaluation Association Members And Prominent Evaluation Theorists And Scholars, Satoshi Ozeki

Research and Creative Activities Poster Day

• Evaluation is a relatively new, practice-based field
• Evaluation scholars lead the field by presenting their theories
• Evaluation theories are not based on empirical evidence
• Empirical investigation is required to establish the field of evaluation
• There were calls for more research on evaluation (RoE)
• The number of studies on RoE has increased in the past decade
• It is unknown whether RoE is important in the evaluation community

Data-Adaptive Inference Of The Optimal Treatment Rule And Its Mean Reward. The Masked Bandit, Antoine Chambaz, Wenjing Zheng, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

This article studies the data-adaptive inference of an optimal treatment rule. A treatment rule is an individualized treatment strategy in which treatment assignment for a patient is based on her measured baseline covariates. Eventually, a reward is measured on the patient. We also infer the mean reward under the optimal treatment rule. We do so in the so called non-exceptional case, i.e., assuming that there is no stratum of the baseline covariates where treatment is neither beneficial nor harmful, and under a companion margin assumption.

Our pivotal estimator, whose definition hinges on the targeted minimum loss estimation (TMLE) principle, actually …

A Weighted Instrumental Variable Estimator To Control For Instrument-Outcome Confounders, Douglas Lehmann, Yun Li, Rajiv Saran, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

No abstract provided.

Flesch-Kincaid Reading Grade Level Re-Examined: Creating A Uniform Method For Calculating Readability On A Certification Exam, Emily Neuhoff, Kristiana M. Feeser, Kayla Sutherland, Thomas Hovatter

Online Journal for Workforce Education and Development

Abstract

Objective: This study attempted to establish a consistent measurement technique of the readability of a state-wide Certified Nursing Assistant’s (CNA) certification exam. Background: Monitoring the readability level of an exam helps ensure all test versions do not exceed the maximum reading level of the exam, and that knowledge of the subject matter, rather than reading ability, is being assessed. Method: A two part approach was used to specify and evaluate readability. First, two methods (Microsoft Word^® (MSW) software and published readability formulae) were used to calculate Flesch Reading Ease (FRE) and Flesch-Kincaid Reading Grade Level (FKRGL) for multiple …

Recommendation To Use Exact P-Values In Biomarker Discovery Research, Margaret Sullivan Pepe, Matthew F. Buas, Christopher I. Li, Garnet L. Anderson

UW Biostatistics Working Paper Series

Background: In biomarker discovery studies, markers are ranked for validation using P-values. Standard P-value calculations use normal approximations that may not be valid for small P-values and small sample sizes common in discovery research.

Methods: We compared exact P-values, valid by definition, with normal and logit-normal approximations in a simulated study of 40 cases and 160 controls. The key measure of biomarker performance was sensitivity at 90% specificity. Data for 3000 uninformative markers and 30 true markers were generated randomly, with 10 replications of the simulation. We also analyzed real data on 2371 antibody array markers …

A Statistical Analysis Of Hurricanes In The Atlantic Basin And Sinkholes In Florida, Joy Marie D'Andrea

USF Tampa Graduate Theses and Dissertations

Beaches can provide a natural barrier between the ocean and inland communities, ecosystems, and resources. These environments can move and change in response to winds, waves, and currents. When a hurricane occurs, these changes can be rather large and possibly catastrophic. The high waves and storm surge act together to erode beaches and inundate low-lying lands, putting inland communities at risk. There are thousands of buoys in the Atlantic Basin that record and update data to help predict climate conditions in the state of Florida. The data that was compiled and used into a larger data set came from two …

The Reliability Of Crowdsourcing: Latent Trait Modeling With Mechanical Turk, Matt Baucum, Steven Rouse Dr., Cindy Miller-Perrin, Elizabeth Mancuso Dr.

Seaver College Research And Scholarly Achievement Symposium

Mechanical Turk, an online crowdsourcing platform, has recently received increased attention in the social sciences as studies continue to suggest its viability as a source for reliable experimental data. Given the ease with which large samples can be quickly and inexpensively gathered, it is worth examining whether Mechanical Turk can provide accurate experimental data for methodologies requiring such large samples. One such methodology is Item Response Theory, a psychometric paradigm that defines test items by a mathematical relationship between a respondent’s ability and the probability of item endorsement. To test whether Mechanical Turk can serve as a reliable source of …

Modelling Latent Variables For Bayesian Networks, Charles Cain

Undergraduate Research Symposium 2016

Bayesian Networks are networks of interconnected variables used to explain causal relationships with conditional probability. Latent variables or hidden variables are variables that cannot be directly measured, like depression or physical activity. They can be used inside of a Bayesian Network. This research looks at latent variables as a weighted sum of observed variables. We use these modeled latent variables as continuous variables in a Bayesian Network. As an example, we look at a Bayesian Network of the causation of diabetes using data from the National Health and Nutrition Examination Survey (NHANES) that is publicly available from the CDC and …

Section Abstracts: Statistics

Virginia Journal of Science

Abstracts of the Statistics Section for the 94th Annual Virginia Academy of Science Meeting, May 18-20, 2016, at University of Mary Washington, Fredericksburg, VA.

Does Academic Performance Predict Workplace Productivity?, Jodie-Gaye Hunter

Honors Projects in Economics

This research examines if college GPA affects productivity and compensation in the workplace. It uses data collected from a survey of approximately 23,000 Bryant University graduates in different stages of their career. About 10 percent of the alumni surveyed completed the survey. The econometric model used in this study allows estimating the effect of GPA on income after controlling for various demographic and socioeconomic variables, including education, major, occupation, gender, among others. The empirical work provides evidence that GPA has a positive and statistically significant impact on workplace productivity for females, but GPA seems to be a weaker predictor of …

User-Centric Workload Analytics: Towards Better Cluster Management, Suhas Raveesh Javagal

Open Access Theses

Effective management of computing clusters and providing a high quality customer support is not a trivial task. Due to rise of community clusters there is an increase in the diversity of workloads and the user demographic. Owing to this and privacy concerns of the user, it is difficult to identify performance issues, reduce resource wastage and understand implicit user demands. In this thesis, we perform in-depth analysis of user behavior, performance issues, resource usage patterns and failures in the workloads collected from a university-wide community cluster and two clusters maintained by a government lab. We also introduce a set of …

Is Metabolism Goal-Directed? Investigating The Validity Of Modeling Biological Systems With Cybernetic Control Via Omic Data, Frank T. Devilbiss

Open Access Dissertations

Cybernetic models are uniquely juxtaposed to other metabolic modeling frameworks in that they describe the time-dependent regulation of cellular reactions in terms of dynamic "metabolic goals." This approach contrasts starkly with purely mechanistic descriptions of metabolic regulation which seek to explain metabolic processes in high resolution — a clearly daunting undertaking. Over a span of three decades, cybernetic models have been used to predict metabolic phenomena ranging from resource consumption in mixed-substrate environments to intracellular reaction fluxes of intricate metabolic networks. While the cybernetic approach has been validated in its utility for the prediction of metabolic phenomena, its central feature, …

Introduction To Statistics, German Vargas, Jose Lugo, Laura Lynch, Jamil Mortada, Treg Thompson, Victor Vega

Mathematics Grants Collections

This Grants Collection for Introduction to Statistics was created under a Round Two ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Basic Statistics (Albany State University), Zephyrinus Okonkwo, Anilkumar Deverapu

Mathematics Grants Collections

This Grants Collection for Basic Statistics was created under a Round Four ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Elementary Statistics, Jared Schlieper, Greg Knofczynski, Michael Tiemeyer

Mathematics Grants Collections

This Grants Collection for Elementary Statistics was created under a Round Four ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Broadening The Impact And Effectiveness Of Simulation-Based Curricula For Introductory Statistics, Nathan L. Tintle, Beth Chance, George Cobb, Allan Rossman, Soma Roy, Todd Swanson, Jill Vanderstoep

Faculty Work Comprehensive List

The demands for a statistically literate society are increasing, and the introductory statistics course “Stat 101” remains the primary venue for learning statistics for the majority of high school and undergraduate students. After three decades of very fruitful activity in the areas of pedagogy and assessment, but with comparatively little pressure for rethinking the content of this course, the statistics education community has recently turned its attention to focusing on simulation-based methods, including bootstrapping and permutation tests, to illustrate core concepts of statistical inference within the context of the overall statistical investigative process. This new focus presents an opportunity to …

Uncovering Local Trends In Genetic Effects Of Multiple Phenotypes Via Functional Linear Models, Olga A. Vsevolozhskaya, Dmitri V. Zaykin, David A. Barondess, Xiaoren Tong, Sneha Jadhav, Qing Lu

Biostatistics Faculty Publications

Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear …

Separation Of Parallel Encoded Complex-Valued Slices (Specs) From A Single Complex-Valued Aliased Coil Image, Daniel B. Rowe, Iain P. Bruce, Andrew S. Nencka, James S. Hyde, Mary C. Kociuba

Mathematics, Statistics and Computer Science Faculty Research and Publications

Purpose

Achieving a reduction in scan time with minimal inter-slice signal leakage is one of the significant obstacles in parallel MR imaging. In fMRI, multiband-imaging techniques accelerate data acquisition by simultaneously magnetizing the spatial frequency spectrum of multiple slices. The SPECS model eliminates the consequential inter-slice signal leakage from the slice unaliasing, while maintaining an optimal reduction in scan time and activation statistics in fMRI studies.

Materials and Methods

When the combined k-space array is inverse Fourier reconstructed, the resulting aliased image is separated into the un-aliased slices through a least squares estimator. Without the additional spatial information from …

Roles Of A Teacher And Researcher During In Situ Professional Development Around The Implementation Of Mathematical Modeling Tasks, Hyunyi Jung, Corey Brady

Mathematics, Statistics and Computer Science Faculty Research and Publications

Partnership with teachers for professional development has been considered beneficial because of the potential of collaborative work in the teacher’s own classroom to be relevant to practice. From this perspective, both teachers and researchers can draw on their own expertise and work as authentic partners. In this study, we address the need for such collaboration and focus on how a teacher and a researcher performed their roles when collaboratively implementing mathematical modeling tasks within a context of in situ professional development. Using multi-tier design-based research, as a framework, a researcher worked in a teacher’s classroom to implement a series of …

Implementation And Validation Of A Probabilistic Open Source Baseball Engine (Posbe): Modeling Hitters And Pitchers, Rhett Tracy Schaefer

Open Access Theses

This manuscript details the implementation and validation of an open source probabilistic baseball engine (POSBE) that focuses on the hitter and pitcher model of the simulation. The simulation produced outcomes that parallel those observed in actual professional Major League Baseball games. The observed data were taken from the nineteen games played between the New York Yankees (NYY) and Boston Red Sox (BOS) during the 2015 season. The potential hitter/pitcher outcomes of interest were singles, doubles, triples, homeruns, walks, hit-by-pitch, and strikeouts. The nineteen game series was simulated 1000 times, resulting in a total of 19,000 simulations. The eighteen hitters and …

Case-Oriented Pathways Analysis In Pancreatic Adenocarcinoma Using Data From A Sleeping Beauty Transposon Mutagenesis Screen, Yen Yi Ho, Timothy K. Starr, Rebecca S. Larue, David A. Largaespada

Faculty Publications

Background: Mutation studies of pancreatic ductal adenocarcinoma (PDA) have revealed complicated heterogeneous genomic landscapes of the disease. These studies cataloged a number of genes mutated at high frequencies, but also report a very large number of genes mutated in lower percentages of tumors. Taking advantage of a well-established forward genetic screening technique, with the Sleeping Beauty (SB) transposon, several studies produced PDA and discovered a number of common insertion sites (CIS) and associated genes that are recurrently mutated at high frequencies. As with human mutation studies, a very large number of genes were found to be altered by transposon insertion …

Empirical Evaluation Of Different Features Of Design In Confirmatory Factor Analysis, Deyab Almaleki

Dissertations

Factor analysis (FA) is the study of variance within a group. Within-subject variance (WSV) is affected by multiple features in a study context, such as: the study experimental design (ED) and sampling design (SD), thus anything that influences or changes variance may affect the conclusions related to FA.

The aim of this study was to provide empirical evaluation of the influence of different aspects of ED and SD on WSV in the context of FA in terms of model precision and model estimate stability. Four Monte Carlo population correlation matrices were hypothesized based on different communality magnitudes (high, moderate, low, …

Bivariate Negative Binomial Hurdle With Random Spatial Effects, Robert Mcnutt

Dissertations

Count data with excess zeros widely occur in ecology, epidemiology, marketing, and many other disciplines. Mixture distributions consisting of a point mass at zero and a separate discrete distribution are often employed in regression models to account for excessive zero observations in the data. While Poisson models are very popular for count data, Negative Binomial models provide greater flexibility due to their ability to account for overdispersion.

This research focuses on developing a method for analyzing bivariate count data with excess zeros collected over a lattice. A bivariate Zero-Inflated Negative Binomial Hurdle (ZINBH) regression model with spatial random effects is …

Bayesian Rank Based Methods For Linear And Generalized Linear Models, James Kodzo Dzikunu

Dissertations

A Bayesian Rank Based Method for linear models is developed in this research. The estimation of the regression coefficients is based on the full conditional distributions utilizing a rank based initial fit. The data likelihood is based on the asymptotic distribution of the gradient function and the asymptotic linearity of this rank-based procedure. Prior distributions are put on regression coefficient(s) and scale parameter(s). The effects of different priors on this scale parameter(s) are studied. Using these full conditional distributions, the estimates are obtained by a Markov Chain Monte-Carlo (MCMC) procedure. The results of our simulation studies show that these Bayesian …

One-Step Targeted Minimum Loss-Based Estimation Based On Universal Least Favorable One-Dimensional Submodels, Mark J. Van Der Laan, Susan Gruber

U.C. Berkeley Division of Biostatistics Working Paper Series

Consider a study in which one observes n independent and identically distributed random variables whose probability distribution is known to be an element of a particular statistical model, and one is concerned with estimation of a particular real valued pathwise differentiable target parameter of this data probability distribution. The targeted maximum likelihood estimator (TMLE) is an asymptotically efficient substitution estimator obtained by constructing a so called least favorable parametric submodel through an initial estimator with score, at zero fluctuation of the initial estimator, that spans the efficient influence curve, and iteratively maximizing the corresponding parametric likelihood till no more updates …

Modeling And Survival Analysis Of Breast Cancer: A Statistical, Artificial Neural Network, And Decision Tree Approach, Venkateswara Rao Mudunuru

USF Tampa Graduate Theses and Dissertations

Survival analysis today is widely implemented in the fields of medical and biological sciences, social sciences, econometrics, and engineering. The basic principle behind the survival analysis implies to a statistical approach designed to take into account the amount of time utilized for a study period, or the study of time between entry into observation and a subsequent event. The event of interest pertains to death and the analysis consists of following the subject until death. Events or outcomes are defined by a transition from one discrete state to another at an instantaneous moment in time. In the recent years, research …

Maximum Likelihood Based Analysis Of Equally Spaced Longitudinal Count Data With Specified Marginal Means, First-Order Antedependence, And Linear Conditional Expectations, Victoria Gamerman, Matthew Guerra, Justine Shults

UPenn Biostatistics Working Papers

This manuscript implements a maximum likelihood based approach that is appropriate for equally spaced longitudinal count data with over-dispersion, so that the variance of the outcome variable is larger than expected for the assumed Poisson distribution. We implement the proposed method in the analysis of two data sets and make comparisons with the semi-parametric generalized estimating equations (GEE) approach that incorrectly ignores the over-dispersion. The simulations demonstrate that the proposed method has better small sample efficiency than GEE. We also provide code in R that can be used to recreate the analysis results that we provide in this manuscript.