Longitudinal Data Analysis and Time Series Commons^™

A Bayesian Framework For Estimating Seismic Wave Arrival Time, Hua Zhong

Graduate Theses and Dissertations

Because earthquakes have a large impact on human society, statistical methods for better studying earthquakes are required. One characteristic of earthquakes is the arrival time of seismic waves at a seismic signal sensor. Once we can estimate the earthquake arrival time accurately, the earthquake location can be triangulated, and assistance can be sent to that area correctly. This study presents a Bayesian framework to predict the arrival time of seismic waves with associated uncertainty. We use a change point framework to model the different conditions before and after the seismic wave arrives. To evaluate the performance of the model, we …

Feasibility Of Multi-Year Forecast For The Colorado River Water Supply: Time Series Modeling, Brian Plucinski

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

The Colorado River is one of the largest resources for water in the United States, as well as being an important asset to the economy. Previous studies have shown a connection between the Great Salt Lake and the Colorado River. This study used time series analysis to build models to predict the water supply of the Colorado River ten years out. These models used data from the Colorado River in addition to Great Salt Lake water elevation. Several models suggest a decline in water supply from 2013 – 2020, before starting to increase. These predictions differ from predictions published by …

Rates Of Relative Sea Level Rise Along The United States East Coast, Jesse N. Beckman, Joseph E. Garcia

Virginia Journal of Science

Recent studies have indicated that some coastal areas, including the East Coast of the United States, are experiencing higher rates of sea level rise than the global average. Rates of relative sea level rise are affected by changes in ocean dynamics, as well as by surface elevation fluctuations due to local land subsidence or uplift. In this study, we derived long-term trends in annual mean relative sea level using tide gauge data obtained from the Permanent Service for Mean Sea Level for stations along the United States East Coast. Stations were grouped by location into the Northeast, Mid-Atlantic, and Southeast …

Feature Selection For Longitudinal Data By Using Sign Averages To Summarize Gene Expression Values Over Time, Suyan Tian, Chi Wang

Biostatistics Faculty Publications

With the rapid evolution of high-throughput technologies, time series/longitudinal high-throughput experiments have become possible and affordable. However, the development of statistical methods dealing with gene expression profiles across time points has not kept up with the explosion of such data. The feature selection process is of critical importance for longitudinal microarray data. In this study, we proposed aggregating a gene’s expression values across time into a single value using the sign average method, thereby degrading a longitudinal feature selection process into a classic one. Regularized logistic regression models with pseudogenes (i.e., the sign average of genes across time as predictors) …

Spatiotemporal Dynamics Of Nitrogen And Carbon Biogeochemistry In A Wetland-Stream Sequence, Patrick E. Hurley

Graduate Student Theses, Dissertations, & Professional Papers

Studies of aquatic ecosystems often segregate streams from the influential ponds, lakes, and wetland zones that act as important transitions between terrestrial and fluvial systems. Across the aquatic landscape, these zones interact to form linked ecosystems that function as discrete nutrient processing domains, shifting biogeochemical signals due to spatial and temporal variability in hydrologic and biologic controls. Using a mass-balance approach, we profiled nutrient dynamics along a 23-km wetland-stream sequence over three seasons. Hydrologic, morphologic, and biologic conditions, as well as landscape attributes, were quantified to determine potential controls on biogeochemical cycling in a tributary of the Upper Clark Fork …

Bayesian Nonparametric Analysis Of Longitudinal Data With Non-Ignorable Non-Monotone Missingness, Yu Cao

Theses and Dissertations

In longitudinal studies, outcomes are measured repeatedly over time, but in reality clinical studies are full of missing data points of monotone and non-monotone nature. Often this missingness is related to the unobserved data so that it is non-ignorable. In such context, pattern-mixture model (PMM) is one popular tool to analyze the joint distribution of outcome and missingness patterns. Then the unobserved outcomes are imputed using the distribution of observed outcomes, conditioned on missing patterns. However, the existing methods suffer from model identification issues if data is sparse in specific missing patterns, which is very likely to happen with a …

Toward Using High-Frequency Coastal Radars For Calibration Of S-Ais Based Ocean Vessel Tracking Models, Ben Freidrich

Theses and Dissertations (Comprehensive)

Most of the world relies on ships for transportation, shipping, and tourism. Automatic Identification System messages are transmitted from ships and provide a wealth of positional data on these open ocean vessels. This data is being utilized to determine the optimal path for ships, as well as predicting where a ship may be going in the near future. It has only been in the past decade that Automatic Identification Systems (AIS) signals have been easily received with satellites (S-AIS) so there have been few studies that look at using available information and pairing it with the new abundance of ship …

On Cluster Robust Models, José Bayoán Santiago Calderón

CGU Theses & Dissertations

Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches …

Statistical Methods For Mixed Frequency Data Sampling Models, Yun Liu

Dissertations, Master's Theses and Master's Reports

The MIDAS models are developed to handle different sampling frequencies in one regression model, preserving information in the higher sampling frequency. Time averaging has been the traditional parametric approach to handle mixed sampling frequencies. However, it ignores information potentially embedded in high frequency. MIDAS regression models provide a concise way to utilize additional information in HF variables. While a parametric MIDAS model provides a parsimonious way to summarize information in HF data, nonparametric models would maintain more flexibility at the expense of the computational complexity. Moreover, one parametric form may not necessarily be appropriate for all cross-sectional subjects. This thesis …

Global Warming Statistical Analysis, Jared Skinner

Williams Honors College, Honors Research Projects

This paper will investigate global warming and its effects on natural disasters. I will review the historic movements of climate change and activism, as well as the current discussions surrounding global warming. Secondly, I will examine various datasets, paying attention to the severity and frequency of specific natural disasters. I will then touch briefly on the topic of catastrophe modeling as it relates to the increased risk and losses associated with the discussed natural disasters and how those put the problem of global warming in a framework which financial and government institutions can grasp. I will also be analyzing economic …

Bayesian Analysis For The Intraclass Model And For The Quantile Semiparametric Mixed-Effects Double Regression Models, Duo Zhang

Dissertations, Master's Theses and Master's Reports

This dissertation consists of three distinct but related research projects. The first two projects focus on objective Bayesian hypothesis testing and estimation for the intraclass correlation coefficient in linear models. The third project deals with Bayesian quantile inference for the semiparametric mixed-effects double regression models. In the first project, we derive the Bayes factors based on the divergence-based priors for testing the intraclass correlation coefficient (ICC). The hypothesis testing of the ICC is used to test the uncorrelatedness in multilevel modeling, and it has not well been studied from an objective Bayesian perspective. Simulation results show that the two sorts …

Latent Growth Model Approach To Characterize Maternal Prenatal Dna Methylation Trajectories, Dana Lapato

Theses and Dissertations

Background. DNA methylation (DNAm) is a removable chemical modification to the DNA sequence intimately associated with genomic stability, cellular identity, and gene expression. DNAm patterning reflects joint contributions from genetic, environmental, and behavioral factors. As such, differences in DNAm patterns may explain interindividual variability in risk liability for complex traits like major depression (MD). Hundreds of significant DNAm loci have been identified using cross-sectional association studies. This dissertation builds on that foundational work to explore novel statistical approaches for longitudinal DNAm analyses. Methods. Repeated measures of genome-wide DNAm and social and environmental determinants of health were collected up to six …

Variable Selection In Accelerated Failure Time (Aft) Frailty Models: An Application Of Penalized Quasi-Likelihood, Sarbesh R. Pandeya

College of Graduate Studies: Theses & Dissertations

Variable selection is one of the standard ways of selecting models in large scale datasets. It has applications in many fields of research study, especially in large multi-center clinical trials. One of the prominent methods in variable selection is the penalized likelihood, which is both consistent and efficient. However, the penalized selection is significantly challenging under the influence of random (frailty) covariates. It is even more complicated when there is involvement of censoring as it may not have a closed-form solution for the marginal log-likelihood. Therefore, we applied the penalized quasi-likelihood (PQL) approach that approximates the solution for such a …

A Logitudinal Feature Selection Method Identifies Relevant Genes To Distinguish Complicated Injury And Uncomplicated Injury Over Time, Suyan Tian, Chi Wang, Howard H. Chang

Biostatistics Faculty Publications

Background: Feature selection and gene set analysis are of increasing interest in the field of bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be utilized to conduct feature selection.

Methods: We adopted a gene set analysis method, the significance analysis of microarray gene set reduction (SAMGSR) algorithm, to carry out feature selection for longitudinal gene expression data.

Results: Using a real-world application and simulated data, it is demonstrated that the proposed SAMGSR extension outperforms other relevant methods. In this study, we illustrate that a gene’s expression profiles over …

Instances Of Influenza In The United States Visualized, Parth Patel

Publications and Research

The Tycho Project collects large data sets related to healthcare and in particular, instances and geographical information of diseases. We look at the instance counts and locations of Influenza from 1919-1951 across the United States. We hope to find seasonal and geographical insight to the spread of the disease.

Role Of Misclassification Estimates In Estimating Disease Prevalence And A Non-Linear Approach To Study Synchrony Using Heart Rate Variability In Chickens, Dola Pathak

Department of Statistics: Dissertations, Theses, and Student Research

Infectious disease assays can be imperfect. When estimating disease prevalence, these imperfections are accounted for by incorporating assay sensitivity and specificity into point and variance estimates. Unfortunately, these accuracy measures are often treated as fixed constants, rather than acknowledging that they are estimates from an assay validation process. The purpose of this study is to show the detrimental effect of not taking into account this sampling variability when samples are obtained through group testing (aka, pooled testing). We show that confidence interval coverage can dramatically decline as the sample size increases for the main sample of interest. As a remedy …

Snakebite Dynamics Of Colombia: Effects Of Precipitation Seasonality Of Incidence, Carlos Cruz

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Monitoring And Evaluating The Influences Of Class V Injection Wells On Urban Karst Hydrology, James Adam Shelley

Masters Theses & Specialist Projects

The response of a karst aquifer to storm events is often faster and more severe than that of a non-karst aquifer. This distinction is often problematic for planners and municipalities, because karst flooding does not typically occur along perennial water courses; thus, traditional flood management strategies are usually ineffective. The City of Bowling Green (CoBG), Kentucky is a representative example of an area plagued by karst flooding. The CoBG, is an urban karst area (UKA), that uses Class V Injection Wells to lessen the severity of flooding. The overall effectiveness, siting, and flooding impact of Injection Wells in UKA’s is …

Longitudinal Data Methods For Evaluating Genome-By-Epigenome Interactions In Families, Justin C. Strickland, I-Chen Chen, Chanung Wang, David W. Fardo

Psychology Faculty Publications

Background: Longitudinal measurement is commonly employed in health research and provides numerous benefits for understanding disease and trait progression over time. More broadly, it allows for proper treatment of correlated responses within clusters. We evaluated 3 methods for analyzing genome-by-epigenome interactions with longitudinal outcomes from family data.

Results: Linear mixed-effect models, generalized estimating equations, and quadratic inference functions were used to test a pharmacoepigenetic effect in 200 simulated posttreatment replicates. Adjustment for baseline outcome provided greater power and more accurate control of Type I error rates than computation of a pre-to-post change score.

Conclusions: Comparison of all modeling approaches indicated …

Association Analyses Of Repeated Measures On Triglyceride And High-Density Lipoprotein Levels: Insights From Gaw20, Saurabh Ghosh, David W. Fardo

Biostatistics Faculty Publications

Background: The GAW20 group formed on the theme of methods for association analyses of repeated measures comprised 4sets of investigators. The provided “real” data set included genotypes obtained from a human whole-genome association study based on longitudinal measurements of triglycerides (TGs) and high-density lipoprotein in addition to methylation levels before and after administration of fenofibrate. The simulated data set contained 200 replications of methylation levels and posttreatment TGs, mimicking the real data set.

Results: The different investigators in the group focused on the statistical challenges unique to family-based association analyses of phenotypes measured longitudinally and applied a wide spectrum of …

The Influence Of Parental Control And Parent-Child Relational Qualities On Adolescent Internet Addiction: A 3-Year Longitudinal Study In Hong Kong, Daniel T. L. Shek, Xiaoqin Zhu, Cecilia M. S. Ma

Pediatrics Faculty Publications

This study investigated how parental behavioral control, parental psychological control, and parent-child relational qualities predicted the initial level and rate of change in adolescent internet addiction (IA) across the junior high school years. The study also investigated the concurrent and longitudinal effects of different parenting factors on adolescent IA. Starting from the 2009/2010 academic year, 3,328 Grade 7 students (M_age = 12.59 ± 0.74 years) from 28 randomly selected secondary schools in Hong Kong responded on a yearly basis to a questionnaire measuring multiple constructs including socio-demographic characteristics, perceived parenting characteristics, and IA. Individual growth curve (IGC) analyses …

Longitudinal Tracking Of Physiological State With Electromyographic Signals., Robert Warren Stallard

Electronic Theses and Dissertations

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A …

Self-Reported Risk And Delinquent Behavior And Problem Behavioral Intention In Hong Kong Adolescents: The Role Of Moral Competence And Spirituality, Daniel T. L. Shek, Xiaoqin Zhu

Pediatrics Faculty Publications

Based on the six-wave data collected from Grade 7 to Grade 12 students (N = 3,328 at Wave 1), this pioneer study examined the development of problem behaviors (risk and delinquent behavior and problem behavioral intention) and the predictors (moral competence and spirituality) among adolescents in Hong Kong. Individual growth curve models revealed that while risk and delinquent behavior accelerated and then slowed down in the high school years, adolescent problem behavioral intention slightly accelerated over time. After controlling the background socio-demographic factors, moral competence and spirituality were negatively associated with risk and delinquent behavior as well as problem …

Building A Better Risk Prevention Model, Steven Hornyak

National Youth Advocacy & Resilience Conference

This presentation chronicles the work of Houston County Schools in developing a risk prevention model built on more than ten years of longitudinal student data. In its second year of implementation, Houston At-Risk Profiles (HARP), has proven effective in identifying those students most in need of support and linking them to interventions and supports that lead to improved outcomes and significantly reduces the risk of failure.

New Approaches To Model Simulated Spatio-Temporal Moran's Index, Nhan Bu, Jennifer Lorio, Norou Diawara, Kumar Das, Lance Waller

Mathematics & Statistics Faculty Publications

The Moran's index is a statistic that measures spatial autocorrelation; it quantifies the degree of dispersion (or clustering) of objects in space. However, when investigating data over a general area, a single global Moran statistic may not give a sufficient summary of the spread, behavior, features or latent surfaces shared by neighboring areas; rather, by partitioning the area and taking the Moran statistic of each divided subareas, we can discover patterns of the local neighbors not otherwise apparent. In this paper, we present a simulation experiment where the local Moran values are computed and a time variable is added to …

Improved Methods And Selecting Classification Types For Time-Dependent Covariates In The Marginal Analysis Of Longitudinal Data, I-Chen Chen

Theses and Dissertations--Epidemiology and Biostatistics

Generalized estimating equations (GEE) are popularly utilized for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, when certain types of time-dependent covariates are presented, these equations can be biased unless an independence working correlation structure is employed. Moreover, in this case regression parameter estimation can be very inefficient because not all valid moment conditions are incorporated within the corresponding estimating equations. Therefore, approaches using the generalized method of moments or quadratic inference functions have been proposed for utilizing all valid moment conditions. However, we have found that …

Investigating The Role Of Prescription Drug Monitoring Programs In Reducing Rates Of Opioid-Related Poisonings, Nathan James Pauly

Theses and Dissertations--Pharmacy

The United States is in the midst of an opioid epidemic. In addition to other system level interventions, almost all states have responded to the crisis by implementing prescription drug monitoring programs (PDMPs). PDMPs are state-level interventions that track the dispensing of Controlled Substances. Data generated at the time of medication dispensing is uploaded to a central data server that may be used to assist in identifying drug diversion, medication misuse, or potentially aberrant prescribing practices.

Prior studies assessing the impact of PDMPs on trends in opioid-related morbidity have often failed to take into account the wide heterogeneity of program …

Improved Standard Error Estimation For Maintaining The Validities Of Inference In Small-Sample Cluster Randomized Trials And Longitudinal Studies, Whitney Ford Tanner

Theses and Dissertations--Epidemiology and Biostatistics

Data arising from Cluster Randomized Trials (CRTs) and longitudinal studies are correlated and generalized estimating equations (GEE) are a popular analysis method for correlated data. Previous research has shown that analyses using GEE could result in liberal inference due to the use of the empirical sandwich covariance matrix estimator, which can yield negatively biased standard error estimates when the number of clusters or subjects is not large. Many techniques have been presented to correct this negative bias; However, use of these corrections can still result in biased standard error estimates and thus test sizes that are not consistently at their …

Using Prescription Drug Monitoring Data To Inform Population Level Analysis Of Opioid Analgesic Utilization, Huong T. T. Luu

Theses and Dissertations--Epidemiology and Biostatistics

Increased opioid analgesic (OA) prescribing has been associated with increased risk of prescription opioid diversion, misuse, and abuse. States established prescription drug monitoring programs (PDMPs) to collect and analyze electronic records for dispensed controlled substances to reduce prescription drug abuse and diversion. PDMP data can be used by prescribers for tracking patient’s history of controlled substance prescribing to inform clinical decisions.

The studies in this dissertation are focused on the less utilized potential of the PDMP data to enhance public health surveillance to monitor OA prescribing and co-prescribing and association with opioid overdose mortality and morbidity. Longitudinal analysis of OA …

Unmasking Cost Growth Behavior: A Longitudinal Study, Cory N. D'Amico, Edward D. White, Jonathan D. Ritschel, Scott R. Kozlak

Faculty Publications

This article examines how cost growth factors (CGF) change over a program’s acquisition life cycle for 36 Department of Defense aircraft programs. Starting from Milestone B, the authors examine CGFs at five gateways: Critical Design Review, First Flight (FF), the end of Developmental Test and Evaluation (DT&E), Initial Operational Capability, and Full Operational Capability. Each CGF is assigned a color rating based upon the program’s cost growth: Green (low), Amber (moderate), or Red (high). Significant findings include dependencies among similar CGF color ratings and cost growth occurring primarily between FF and the end of DT&E during a program’s life cycle.