Statistics and Probability Commons^™

The Influence Of Model Resolution On The Simulated Sensitivity Of North Atlantic Tropical Cyclone Maximum Intensity To Sea Surface Temperature, Sarah Strazzo, James Elsner, Timothy Larow, Hiroyuki Murakami, Michael Wehner, Ming Zhao

Publications

No abstract provided.

Practical Targeted Learning From Large Data Sets By Survey Sampling, Patrice Bertail, Antoine Chambaz, Emilien Joly

U.C. Berkeley Division of Biostatistics Working Paper Series

We address the practical construction of asymptotic confidence intervals for smooth (i.e., pathwise differentiable), real-valued statistical
parameters by targeted learning from independent and identically
distributed data in contexts where sample size is so large that it poses
computational challenges. We observe some summary measure of all data and select a sub-sample from the complete data set by Poisson rejective sampling with unequal inclusion probabilities based on the summary measures. Targeted learning is carried out from the easier to handle sub-sample. We derive a central limit theorem for the targeted minimum loss estimator (TMLE) which enables the construction of …

How The Magnitude Of Prey Genetic Variation Alters Predator-Prey Eco-Evolutionary Dynamics, Michael H. Cortez

Mathematics and Statistics Faculty Publications

Evolution can alter the stability and dynamics of ecological communities; for example, prey evolution can drive cyclic dynamics in predator-prey systems that are not possible in the absence of evolution. However, it is unclear how the magnitude of additive genetic variation in the evolving species mediates those effects. In this study, I explore how the magnitude of prey additive genetic variation determines what effects prey evolution has on the dynamics and stability of predator-prey systems. I use linear stability analysis to decompose the stability of a general eco-evolutionary predator-prey model into components representing the stabilities of the ecological and evolutionary …

Analysis Off Dependent Discrete Choices Using Gaussian Copula, Arjun Poddar

Mathematics & Statistics Theses & Dissertations

A popular tool for analyzing product choices of consumers is the well-known conditional logit discrete choice model. Originally publicized by McFadden (1974), this model assumes that the random components of the underlying latent utility functions of the consumers follow independent Gumbel distributions. However, in practice the independence assumption may be violated and a more reasonable model should account for the dependence of the utilities. In this dissertation we use the Gaussian copula with compound symmetric and autoregressive of order one correlation matrices to construct a general multivariate model for the joint distribution of the utilities. The induced correlations on the …

Computational Modeling Of Facial Response For Detecting Differential Traits In Autism Spectrum Disorders, Manar D. Samad

Electrical & Computer Engineering Theses & Dissertations

This dissertation proposes novel computational modeling and computer vision methods for the analysis and discovery of differential traits in subjects with Autism Spectrum Disorders (ASD) using video and three-dimensional (3D) images of face and facial expressions. ASD is a neurodevelopmental disorder that impairs an individual’s nonverbal communication skills. This work studies ASD from the pathophysiology of facial expressions which may manifest atypical responses in the face. State-of-the-art psychophysical studies mostly employ na¨ıve human raters to visually score atypical facial responses of individuals with ASD, which may be subjective, tedious, and error prone. A few quantitative studies use intrusive sensors on …

Improved Estimation Of Optimal Cut-Off Point Associated With Youden Index Using Ranked Set Sampling, Jingjing Yin, Hani M. Samawi, Daniel Linder

Biostatistics: Faculty Publications

A diagnostic cut-off point of a biomarker measurement is needed for classifying a random subject to be either diseased or healthy. However, the cut-off point is usually unknown and needs to be estimated by some optimization criteria. One important criterion is the Youden index, which has been widely adopted in practice. The Youden index, which is defined as the maximum of (sensitivity + specificity −1), directly measures the largest total diagnostic accuracy a biomarker can achieve. Therefore, it is desirable to estimate the optimal cut-off point associated with the Youden index. Sometimes, taking the actual measurements of a biomarker is …

Complex-Valued Time-Series Correlation Increases Sensitivity In Fmri Analysis, Mary C. Kociuba, Daniel B. Rowe

Mathematics, Statistics and Computer Science Faculty Research and Publications

Purpose

To develop a linear matrix representation of correlation between complex-valued (CV) time-series in the temporal Fourier frequency domain, and demonstrate its increased sensitivity over correlation between magnitude-only (MO) time-series in functional MRI (fMRI) analysis.

Materials and Methods

The standard in fMRI is to discard the phase before the statistical analysis of the data, despite evidence of task related change in the phase time-series. With a real-valued isomorphism representation of Fourier reconstruction, correlation is computed in the temporal frequency domain with CV time-series data, rather than with the standard of MO data. A MATLAB simulation compares the Fisher-z transform …

Effects Of Growth Mindset Training On Undergraduate Statistics Students, Valorie L. Zonnefeld

Faculty Work Comprehensive List

Undergraduate introductory statistics courses have experienced numerous changes in the past century, for instance, increased enrollment and diversification of students required to take the courses. Promising research has been conducted on mathematical mindsets, however, no research is available for introductory statistics courses. This presentation addresses the effect of growth mindset training on students in mathematics.

Guidelines For Assessment And Instruction In Statistics Education (Gaise) College Report 2016, Robert Carver, Michelle Everson, John Gabrosek, Nicholas Horton, Robin Lock, Megan Mocko, Allan Rossman, Ginger Holmes Roswell, Paul Velleman, Jeffrey Witmer, Beverly Wood

Publications

In 2005 the American Statistical Association (ASA) endorsed the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report. This report has had a profound impact on the teaching of introductory statistics in two- and four-year institutions, and the six recommendations put forward in the report have stood the test of time. Much has happened within the statistics education community and beyond in the intervening 10 years, making it critical to re-evaluate and update this important report. For readers who are unfamiliar with the original GAISE College Report or who are new to the statistics education community, the full …

Wartime Construction Project Outcomes As A Function Of Contract Type, Ryan M. Hoff, Gregory D. Hammond, Peter P. Feng, Edward D. White

Faculty Publications

The United States has spent more than $23 billion on construction in Afghanistan since 2001. The dynamic security situation created substantial project uncertainty, and many construction projects used cost-plus-fixed-fee contracts (CPFF) instead of the firm-fixed-price (FFP) norm. Using a dataset of 25 wartime construction projects managed by the Air Force Civil Engineer Center, the authors sought to confirm that both contract types yield project outcomes consistent with the established literature. As expected, they found CPFF contracts had greater cost and schedule growth than FFP. However, they did not find differences regarding as-built quality. Additionally, the authors sought to determine whether …

Bayesian Nonparametric Approaches To Multiple Testing, Density Estimation, And Supervised Learning, William Cipolli Iii

Theses and Dissertations

This dissertation presents methods for several applications of Polya tree models. These novel nonparametric approaches to the problems of multiple testing, density estimation and supervised learning provide an alternative to other parametric and nonparametric models. In Chapter 2, the proposed approximate finite Polya tree multiple testing procedure is very successful in correctly classifying the observations with non-zero mean in a computationally efficient manner; this holds even when the non-zero means are simulated from a mean-zero distribution. Further, the model is capable of this for “interestingly different” observations in the cases where that is of interest. Chapter 3 proposes discrete, and …

Development And Application Of Bayesian Semiparametric Models For Dependent Data, Junshu Bao

Theses and Dissertations

Dependent data are very common in many research fields, such as medicine (repeated measures), finance (time series), traffic (clustered), etc. Effective control/modeling of the dependency among data can enhance the performance of the models and result in better prediction. In many cases, the correlation itself may be of great interest. In this dissertation, we develop novel Bayesian semi-/nonparametric regression models to analyze data with various dependence structures. In Chapter 2, a Bayesian non- parametric multivariate ordinal regression model is proposed to fit drinking behavior survey data from DWI offenders. The responses are two-dimensional ordinal data, drinking frequency and drinking quantity …

Novel Methods For Analyzing Longitudinal Data With Measurement Error In The Time Variable, Caroline Munindi Mulatya

Theses and Dissertations

In some longitudinal studies, the observed time points are often confounded with measurement error due to the sampling conditions, resulting into data with measurement error in the time variable. This type of data occurs mainly in observational studies when the onset of a longitudinal process is unknown or in clinical trials when individual visits do not take place as specified by the study protocol, but are often rounded to coincide with the study protocol. Methodological and inferential implications of error in time varying covariates for both linear and nonlinear models have been studied widely. In this dissertation, we shift attention …

Scalable Collaborative Targeted Learning For High-Dimensional Data, Cheng Ju, Susan Gruber, Samuel D. Lendle, Antoine Chambaz, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the C-TMLE procedure.

The original C-TMLE procedure can be presented as a greedy forward stepwise algorithm. It does not scale well when the number $p$ …

Propensity Score Prediction For Electronic Healthcare Databases Using Super Learner And High-Dimensional Propensity Score Methods, Cheng Ju, Mary Combs, Samuel D. Lendle, Jessica M. Franklin, Richard Wyss, Sebastian Schneeweiss, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a "library" of candidate prediction models. The SL is not restricted to a single prediction model, but uses the strengths of a variety of learning algorithms to adapt to different databases. While the SL has been shown to perform well in a number of settings, it has not been thoroughly evaluated in large electronic healthcare databases that are common in pharmacoepidemiology and comparative effectiveness research. In this study, we applied and evaluated …

Tmle For Marginal Structural Models Based On An Instrument, Boriska Toth, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider estimation of a causal effect of a possibly continuous treatment when treatment assignment is potentially subject to unmeasured confounding, but an instrumental variable is available. Our focus is on estimating heterogeneous treatment effects, so that the treatment effect can be a function of an arbitrary subset of the observed covariates. One setting where this framework is especially useful is with clinical outcomes. Allowing the causal dose-response curve to depend on a subset of the covariates, we define our parameter of interest to be the projection of the true dose-response curve onto a user-supplied working marginal structural model. We …

Predictors Of Neonatal Abstinence Syndrome In Buprenorphine Exposed Newborn: Can Cord Blood Buprenorphine Metabolite Levels Help?, Darshan Shah, Stacy Brown, Nick Hagemeier, Shimin Zheng, Amy Kyle, Jason Pryor, Nilesh Dankhara, Piyuesh Singh

ETSU Faculty Works

Background

Buprenorphine is a semi-synthetic opioid used for the treatment of opioid dependence. Opioid use, including buprenorphine, has been increasing in recent years, in the general population and in pregnant women. Consequently, there has been a rise in frequency of neonatal abstinence syndrome (NAS), associated with buprenorphine use during pregnancy. The purpose of this study was to investigate correlations between buprenorphine and buprenorphine-metabolite concentrations in cord blood and onset of NAS in buprenorphine exposed newborns.

Methods

Nineteen (19) newborns who met inclusion criteria were followed after birth until discharge in a double-blind non-intervention study, after maternal consent. Cord blood and …

A Powerful Statistical Framework For Generalization Testing In Gwas, With Application To The Hchs/Sol, Tamar Sofer, Ruth Heller, Marina Bogomolov, Christy L. Avery, Mariaelisa Graff, Kari E. North, Alex Reiner, Timothy A. Thornton, Kenneth Rice, Yoav Benjamini, Cathy C. Laurie, Kathleen F. Kerr

UW Biostatistics Working Paper Series

In GWAS, “generalization” is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. The standard for reporting findings from a GWAS requires a two-stage design, in which discovered associations are replicated in an independent follow-up study. Current practices for declaring generalizations rely on testing associations while controlling the Family Wise Error Rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. While this approach limits false generalizations, we show that it does not guarantee control over the FWER or False Discovery Rate (FDR) of …

A Randomized, Double-Blind, Placebo-Controlled Phase Ii Trial Investigating The Safety And Immunogenicity Of Modified Vaccinia Ankara Smallpox Vaccine (Mva-Bn®) In 56-80-Year-Old Subjects, Richard N. Greenberg, Christine M. Hay, Jack T. Stapleton, Thomas C. Marbury, Eva Wagner, Eva Kreitmeir, Siegfried Röesch, Alfred Von Krempelhuber, Philip Young, Richard Nichols, Thomas P. Meyer, Darja Schmidt, Josef Weigl, Garth Virgin, Nathaly Arndtz-Wiedemann, Paul Chaplin

Internal Medicine Faculty Publications

Background Modified Vaccinia Ankara MVA-BN^® is a live, highly attenuated, viral vaccine under advanced development as a non-replicating smallpox vaccine. In this Phase II trial, the safety and immunogenicity of Modified Vaccinia Ankara MVA-BN^® (MVA) was assessed in a 56–80 years old population.

Methods MVA with a virus titer of 1 x 10⁸ TCID₅₀/dose was administered via subcutaneous injection to 56–80 year old vaccinia-experienced subjects (N = 120). Subjects received either two injections of MVA (MM group) or one injection of Placebo and one injection of MVA (PM group) four weeks apart. Safety was evaluated …

The Effects Of Age And Gender On Pedestrian Traffic Injuries: A Random Parameters And Latent Class Analysis, Tatok Raharjo Raharjo

USF Tampa Graduate Theses and Dissertations

Pedestrians are vulnerable road users because they do not have any protection while they walk. They are unlike cyclists and motorcyclists who often have at least helmet protection and sometimes additional body protection (in the case of motorcyclists with body-armored jackets and pants). In the US, pedestrian fatalities are increasing and becoming an ever larger proportion of overall roadway fatalities (NHTSA, 2016), thus underscoring the need to study factors that influence pedestrian-injury severity and potentially develop appropriate countermeasures. One of the critical elements in the study of pedestrian-injury severities is to understand how injuries vary across age and gender ‒ …