Statistics and Probability Commons^™

Likelihood Based Estimation Of Logistic Structural Nested Mean Models With An Instrumental Variable, Roland A. Matsouaka, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

Using Remote Sensing Data To Predict The Spread Of Mosquito Borne Disease, Mary Ellen O'Donnell, Erika Podest

STAR Program Research Presentations

There is interest in how environmental variables derived from satellite data such as temperature, vegetation cover, and precipitation correlate to vector borne disease occurrence such as malaria and dengue fever. This study will be carried out using a decision tree based open source software called Random Forests to find correlations between the remote sensing variables and mosquito abundance. Software will be written in C# to take large amounts of data from the NASA satellite database and automatically format it for the Random Forest Software input. Correlations found, using Random Forests, between disease incidence and the variables can be used as …

The Path To The Sea: Leatherback Hatchling Orientation At Sandy Point National Wildlife Refuge, Christina Macmillan, Kelly Stewart

STAR Program Research Presentations

Once sea turtle hatchlings emerge from their nest, they must find their way to the ocean by using cues such as a bright horizon and the slope of the beach. While moving toward the water, hatchlings often must navigate past predators and through vegetation, sticks, footprints in the sand, and other dangers such as ghost crab holes. Sometimes hatchlings become confused (or disoriented) and turn in circles to find the right route to the water. Sea turtle hatchlings also may become disoriented as a result of human impacts such as town lights or trash. The purpose of our experiment was …

Light Pollution Research Through Citizen Science, John Kanemoto

STAR Program Research Presentations

Light pollution (LP) can disrupt and/or degrade the health of all living things, as well as, their environments. The goal of my research at the NOAO was to check the accuracy of the citizen science LP reporting systems entitled: Globe at Night (GaN), Dark Sky Meter (DSM), and Loss of the Night (LoN). On the GaN webpage, the darkness of the night sky (DotNS) is reported by selecting a magnitude chart. Each magnitude chart has a different density/number of stars around a specific constellation. The greater number of stars implies a darker night sky. Within the DSM iPhone application, a …

Tuffoam Density Variation Through Radiography, Sergio Contreras Esquivel

STAR Program Research Presentations

TufFoam is a low density, high impact tolerant polyurethane foam with good high voltage breakdown developed at the Sandia National Laboratories. Identically shaped samples have been produced with various formulations and production conditions. Eight different variables were studied. Our aim is to compare the density variation of the material when produced and processed under distinct conditions. The density of TufFoam is determined using radiographic imaging. We extract the foam density by fitting the radiographic density of each step in a plastic step wedge with 10 steps from 0.1 to 1.0 inches and applying the resulting equation to convert each pixel …

Lifestyle And Metformin Interventions Have A Durable Effect To Lower Crp And Tpa Levels In The Diabetes Prevention Program Except In Those Who Develop Diabetes., Ronald B Goldberg, Marinella G Temprosa, Kieren J Mather, Trevor J Orchard, Abbas E Kitabchi, Karol E Watson

GW Biostatistics Center

OBJECTIVE: We evaluate whether lifestyle and metformin interventions used to prevent diabetes have durable effects on markers of inflammation and coagulation and whether the effects are influenced by the development of diabetes.

RESEARCH DESIGN AND METHODS: The Diabetes Prevention Program was a controlled clinical trial of 3,234 subjects at high risk for diabetes who were randomized to lifestyle, metformin, or placebo interventions for 3.4 years. Diabetes was diagnosed semiannually by fasting glucose and annually by oral glucose tolerance testing. In addition to baseline testing, anthropometry was performed every 6 months; fasting insulin yearly; and hs-CRP, tissue plasminogen activator (tPA), and …

Inflammatory Breast Cancer Clusters: A Hypothesis, Paul H. Levine, Salman Hashmi, Ashley A. Minaei, Carmela Veneroso

Epidemiology Faculty Publications

Reproduced with permission of Baishideng Publishing Group, World Journal of Clinical Oncology.

Quantile Regression For Climate Data, Dilhani Marasinghe

All Theses

Quantile regression is a developing statistical tool which is used to explain the relationship between response and predictor variables. This thesis describes two examples of climatology using quantile regression.Our main goal is to estimate derivatives of a conditional mean and/or conditional quantile function. We introduce a method to handle autocorrelation in the framework of quantile regression and used it with the temperature data. Also we explain some properties of the tornado data which is non-normally distributed. Even though quantile regression provides a more comprehensive view, when talking about residuals with the normality and the constant variance assumption, we would prefer …

Penalized Regressions For Variable Selection Model, Single Index Model And An Analysis Of Mass Spectrometry Data., Yubing Wan

Electronic Theses and Dissertations

The focus of this dissertation is to develop statistical methods, under the framework of penalized regressions, to handle three different problems. The first research topic is to address missing data problem for variable selection models including elastic net (ENet) method and sparse partial least squares (SPLS). I proposed a multiple imputation (MI) based weighted ENet (MI-WENet) method based on the stacked MI data and a weighting scheme for each observation. Numerical simulations were implemented to examine the performance of the MIWENet method, and compare it with competing alternatives. I then applied the MI-WENet method to examine the predictors for the …

A Study Of Joinpoint Models For Longitudinal Data, Libo Zhou

UNLV Theses, Dissertations, Professional Papers, and Capstones

In many medical studies, data are collected simultaneously on multiple biomarkers from each individual. Levels of these biomarkers are measured periodically over certain time duration, giving rise to longitudinal trajectories. The subjects under study may also be subject to dropout due to several competing causes, the likelihood of which may be affected by the levels of these biomarkers. In this dissertation, we investigate flexible Bayesian modeling of such data, taking into account any available covariate information as well as possible censoring of the drop-out times. We propose joint models for multiple biomarkers with multiple causes of dropout. Our proposed models …

Comparison Of Hazard, Odds And Risk Ratio In The Two-Sample Survival Problem, Benedict P. Dormitorio

Dissertations

Cox proportional hazards is the standard method for analyzing treatment efficacy when time-to-event data is available. In the absence of time-to-event, investigators may use logistic regression which only requires relative frequencies of events, or Poisson regression which requires only interval-summarized frequency tables of time-to-event. When event frequencies are used instead of time-to-events, does it always result in a loss in power?

We investigate the relative performance of the three methods. In particular, we compare the power of tests based on the respective effect-size estimates (1)hazard ratio (HR), (2)odds ratio (OR), and (3)risk ratio (RR). We use a variety of survival …

General Approaches For Combining Multiple Rare Variant Associate Tests Provide Improved Power Across A Wider Range Of Genetic Architecture, Nathan L. Tintle, Brian Greco, Allison Hainline, Keli Liu, Jaron Arbet, Alejandra Benitez, Kelsey Grinde

Faculty Work Comprehensive List

In the wake of the widespread availability of genome sequencing data made possible by way of nextgeneration technologies, a flood of gene‐based rare variant tests have been proposed. Most methods claim superior power against particular genetic architectures. However, an important practical issue remains for the applied researcher—namely, which test should be used for a particular association study which may consider multiple genes and/or multiple phenotypes. Recently, tests have been proposed which combine individual tests to minimize power loss while improving the robustness to a wide range of genetic architectures. In our analysis, we propose an expansion of these approaches, by …

Asymptotic Behavior Of Finite-Time Ruin Probability In A By-Claim Risk Model With Constant Interest Rate, Lei Wang

Master's Theses

Enlightened by the results of Li [8] and Wang [19], we study the ruin probability of a renewal risk model with constant interest rate and by-claim parts. We assume that the claim size and the inter-arrival time satisfy a certain dependent structure with some additional assumptions on their distribution functions. Furthermore, we give relevant preparation of theory and compare several existing risk models and dependent structures. In this way, we present our result and prove it.

Genetic Predictors Of Metabolic Side Effects Of Diuretic Therapy, Jorge L. Del Aguila

Dissertations and Theses (Open Access)

Thiazide diuretics are a recommended first-line monotherapy for hypertension (i.e.SBP>140 mmHg or DBP>90 mmHg). Even so, diuretics are associated with adverse metabolic side effects, such as hyperlipidemia, hyperglycemia and hypokalemia which increase the risk of developing type II diabetes. This thesis used three analytical strategies to identify and quantify genetic factors that contribute to the development of adverse metabolic effects due to thiazide diuretic treatment. I performed a genome-wide association study (GWAS) and meta-analysis of the change in fasting plasma glucose and triglycerides in response to HCTZ from two different clinical trials: the Pharmacogenomic Evaluation of Antihypertensive Responses …

Convergence Of A Reinforcement Learning Algorithm In Continuous Domains, Stephen Carden

All Dissertations

In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and actions have been well studied, and there exist algorithms capable of producing a sequence of policies which converge to an optimal policy with probability one. Convergence guarantees for problems with continuous states also exist. Until recently, no online algorithm for continuous states and continuous actions has been proven to produce optimal policies. This Dissertation contains the results of research into reinforcement learning algorithms for problems in which both the state and action spaces are continuous. The problems to be solved are introduced formally as …

Analyses Of 2002-2013 China’S Stock Market Using The Shared Frailty Model, Chao Tang

Electronic Theses and Dissertations

This thesis adopts a survival model to analyze China’s stock market. The data used are the capitalization-weighted stock market index (CSI 300) and the 300 stocks for creating the index. We define the recurrent events using the daily return of the selected stocks and the index. A shared frailty model which incorporates the random effects is then used for analyses since the survival times of individual stocks are correlated. Maximization of penalized likelihood is presented to estimate the parameters in the model. The covariates are selected using the Akaike information criterion (AIC) and the variance inflation factor (VIF) to avoid …

Visualizing And Forecasting Box-Office Revenues: A Case Study Of The James Bond Movie Series, Vahan Petrosyan

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

This Master's report deals with the visualization and forecasting of the box-office revenues and some related variables from the James Bond movie Series. Visualization techniques such as time series plots, scatterplot matrices, dotplots, boxplots, histograms, normal quantile plots, parallel coordinates plots, heatmaps, mosaic plots, association plots, and choropleth maps are used to provide some deeper insights into the given dataset. Additionally, the results from an article published in 1997 are reproduced and extended. This article modeled the box-office revenues of the James Bond movie series. Numerous statistical models were examined to obtain the models that are closest to the original …

A General Approach To Detect Gene (G)-Environment (E) Additive Interaction Leveraging G-E Independence In Case-Control Studies, Eric Tchetgen Tchetgen, Tamar Sofer, Benedict H.W. Wong

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Novel Targeted Learning Method For Quantitative Trait Loci Mapping, Hui Wang, Zhongyang Zhang, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We present a novel semiparametric method for quantitative trait loci (QTL) mapping in experimental crosses. Conventional genetic mapping methods typically assume parametric models with Gaussian errors and obtain parameter estimates through maximum likelihood estimation. In contrast with univariate regression and interval mapping methods, our model requires fewer assumptions and also accommodates various machine learning algorithms. Estimation is performed with targeted maximum likelihood learning methods. We demonstrate our semiparametric targeted learning approach in a simulation study and a well-studied barley dataset.

Strategic Focus On 3r Principles Reveals Major Reductions In The Use Of Animals In Pharmaceutical Toxicity Testing, Elin Törnqvist, Anita Annas, Britta Granath, Elisabeth Jalkesten, Ian Cotgreave, Mattias Öberg

Application of Alternative Methods Collection

The principles of the 3Rs, Replacement, Reduction and Refinement, are being increasingly incorporated into legislations, guidelines and practice of animal experiments in order to safeguard animal welfare. In the present study we have studied the systematic application of 3R principles to toxicological research in the pharmaceutical industry, with particular focus on achieving reductions in animal numbers used in regulatory and investigatory in vivo studies. The work also details major factors influencing these reductions including the conception of ideas, cross-departmental working and acceptance into the work process. Data from 36 reduction projects were collected retrospectively from work between 2006 and 2010. …

A Note On The Control Function Approach With An Instrumental Variable And A Binary Outcome, Eric Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Simple Regression-Based Approach To Account For Survival Bias In Birth Outcomes Research, Eric J. Tchetgen Tchetgen, Kelesitse Phiri, Roger Shapiro

Harvard University Biostatistics Working Paper Series

No abstract provided.

Entering The Era Of Data Science: Targeted Learning And The Integration Of Statistics And Computational Data Analysis, Mark J. Van Der Laan, Richard J.C.M. Starmans

U.C. Berkeley Division of Biostatistics Working Paper Series

This outlook article will appear in Advances in Statistics and it reviews the research of Dr. van der Laan's group on Targeted Learning, a subfield of statistics that is concerned with the construction of data adaptive estimators of user-supplied target parameters of the probability distribution of the data and corresponding confidence intervals, aiming to only rely on realistic statistical assumptions. Targeted Learning fully utilizes the state of the art in machine learning tools, while still preserving the important identity of statistics as a field that is concerned with both accurate estimation of the true target parameter value and assessment of …

Control Function Assisted Ipw Estimation With A Secondary Outcome In Case-Control Studies, Tamar Sofer, Marilyn C. Cornelis, Peter Kraft, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

No abstract provided.

Reduced Major Axis Regression: Teaching Alternatives To Least Squares, William V. Harper

Mathematics Faculty Scholarship

The theoretical underpinnings of standard least squares regression analysis are based on the assumption that the independent variable (often thought of as x) is measured without error as a design variable. The dependent variable (often labeled y) is modeled as having uncertainty or error. Both independent and dependent measurements may have multiple sources of error. Thus the underlying least squares regression assumptions can be violated. Reduced Major Axis (RMA) regression is specifically formulated to handle errors in both the x and y variables. It is an excellent topic to teach students the importance of understanding the assumptions underlying the statistical …

What Is Higher Mathematics? Why Is It So Hard To Interpret? What Can Be Done?, John Tabak

Journal of Interpretation

Courses and seminars in higher mathematics are some of the most challenging assignments faced by academic interpreters. Difficulties interpreting higher mathematics can adversely impact the academic and professional aspirations of deaf mathematics students and professionals. This paper discusses the nature of higher mathematics with the goal of identifying what distinguishes higher mathematics from other subjects; it then reviews the history of attempts to sign/interpret higher mathematics with particular attention to current challenges associated with expressing higher mathematics in sign. The final part of the paper discusses strategies for more effectively expressing higher mathematics in American Sign Language.

Statistical Modeling And Prediction Of Hiv/Aids Prognosis: Bayesian Analyses Of Nonlinear Dynamic Mixtures, Xiaosun Lu

USF Tampa Graduate Theses and Dissertations

Statistical analyses and modeling have contributed greatly to our understanding of the pathogenesis of HIV-1 infection; they also provide guidance for the treatment of AIDS patients and evaluation of antiretroviral (ARV) therapies. Various statistical methods, nonlinear mixed-effects models in particular, have been applied to model the CD4 and viral load trajectories. A common assumption in these methods is all patients come from a homogeneous population following one mean trajectories. This assumption unfortunately obscures important characteristic difference between subgroups of patients whose response to treatment and whose disease trajectories are biologically different. It also may lack the robustness against population heterogeneity …

Association Between Class Iii Obesity (Bmi Of 40-59 Kg/M2) And Mortality: A Pooled Analysis Of 20 Prospective Studies, Cari M. Kitahara, Alan J. Flint, Amy Berrington De Gonzalez, Leslie Bernstein, Michelle Brotzman, Kim Robien, +30 Additional Authors

Epidemiology Faculty Publications

Background

The prevalence of class III obesity (body mass index [BMI]≥40 kg/m2) has increased dramatically in several countries and currently affects 6% of adults in the US, with uncertain impact on the risks of illness and death. Using data from a large pooled study, we evaluated the risk of death, overall and due to a wide range of causes, and years of life expectancy lost associated with class III obesity.

Methods and Findings

In a pooled analysis of 20 prospective studies from the United States, Sweden, and Australia, we estimated sex- and age-adjusted total and cause-specific mortality rates (deaths per …

Super-Learning Of An Optimal Dynamic Treatment Rule, Alexander R. Luedtke, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider the estimation of an optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric, beyond possible knowledge about the treatment and censoring mechanisms. We propose data adaptive estimators of this optimal dynamic regime which are defined by sequential loss-based learning under both the blip function and weighted classification frameworks. Rather than \textit{a priori} selecting …

Targeted Learning Of The Mean Outcome Under An Optimal Dynamic Treatment Rule, Mark J. Van Der Laan, Alexander R. Luedtke

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider estimation of and inference for the mean outcome under the optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric beyond possible knowledge about the treatment and censoring mechanism. This contrasts from the current literature that relies on parametric assumptions. We establish that the mean of the counterfactual outcome under the optimal dynamic treatment …