Statistical Models Commons^™

Analyzing Two-Year College Student Success Using Structural Equation Modeling, Jessica Taylor

Graduate Theses, Dissertations, and Capstones

The goal of this study is to more fully understand the scope of community college student success using the principles of mindset, engagement, and college readiness. Using structural equation modeling ensures this study is able to measure the combined effects these concepts have on student success, group differences, and the combined model of student success. Findings suggest student success can be significantly impacted by self-belief and mindset behaviors that can outweigh the initial effect of academically under-prepared students. Groups included in this study are non-traditional students, minority populations, first generation students, and Pell eligible students.

Biodiversity And Distribution Of Benthic Foraminifera In Harrington Sound, Bermuda: The Effects Of Physical And Geochemical Factors On Dominant Taxa, Nam Le

Honors Theses

Harrington Sound, Bermuda, is a nearly enclosed lagoon acting as a subtropical/tropical, carbonate-rich basin in which carbonate sediments, reef patches, and carbonate-producing organisms accumulate. Here, one of the most important calcareous groups is the Foraminifera. Analyses of common benthic orders, including miliolids (Quinqueloculina and Triloculina spp.) and rotaliids (Homotrema rubrum, Elphidium spp., and Ammonia beccarii), are essential in understanding past and present environmental conditions affecting the island's coastal environment. These taxa have been studied previously; however, factors explaining their individual patterns of abundance in the Sound are not well detailed. The goal of this study is …

Modeling Stochastically Intransitive Relationships In Paired Comparison Data, Ryan Patrick Alexander Mcshane

Statistical Science Theses and Dissertations

If the Warriors beat the Rockets and the Rockets beat the Spurs, does that mean that the Warriors are better than the Spurs? Sophisticated fans would argue that the Warriors are better by the transitive property, but could Spurs fans make a legitimate argument that their team is better despite this chain of evidence?

We first explore the nature of intransitive (rock-scissors-paper) relationships with a graph theoretic approach to the method of paired comparisons framework popularized by Kendall and Smith (1940). Then, we focus on the setting where all pairs of items, teams, players, or objects have been compared to …

Data Patterns Discovery Using Unsupervised Learning, Rachel A. Lewis

Electronic Theses and Dissertations

Self-care activities classification poses significant challenges in identifying children’s unique functional abilities and needs within the exceptional children healthcare system. The accuracy of diagnosing a child's self-care problem, such as toileting or dressing, is highly influenced by an occupational therapists’ experience and time constraints. Thus, there is a need for objective means to detect and predict in advance the self-care problems of children with physical and motor disabilities. We use clustering to discover interesting information from self-care problems, perform automatic classification of binary data, and discover outliers. The advantages are twofold: the advancement of knowledge on identifying self-care problems in …

Essays On Mixture Models, Trevor R. Camper

Electronic Theses and Dissertations

When considering statistical scenarios where one can sample from populations that are not of interest for the purposes of a study, bivariate mixture models can be used to study the effect that this missampling can have on parameter estimation. In this thesis, we will examine the behavior that bivariate mixture models have on two statistical constructs: Cronbach's alpha \cite{C51}, and Spearman's rho \cite{S04}. Chapter 1 will introduce notions of mixture models and the definition of bias under mixture models which will serve as the central concept of this thesis. Chapter 2 will investigate a particular psychometric issue known as insufficient …

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

Electronic Theses and 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 …

Transforms In Sufficient Dimension Reduction And Their Applications In High Dimensional Data, Jiaying Weng

Theses and Dissertations--Statistics

The big data era poses great challenges as well as opportunities for researchers to develop efficient statistical approaches to analyze massive data. Sufficient dimension reduction is such an important tool in modern data analysis and has received extensive attention in both academia and industry.

In this dissertation, we introduce inverse regression estimators using Fourier transforms, which is superior to the existing SDR methods in two folds, (1) it avoids the slicing of the response variable, (2) it can be readily extended to solve the high dimensional data problem. For the ultra-high dimensional problem, we investigate both eigenvalue decomposition and minimum …

Composite Nonparametric Tests In High Dimension, Alejandro G. Villasante Tezanos

Theses and Dissertations--Statistics

This dissertation focuses on the problem of making high-dimensional inference for two or more groups. High-dimensional means both the sample size (n) and dimension (p) tend to infinity, possibly at different rates. Classical approaches for group comparisons fail in the high-dimensional situation, in the sense that they have incorrect sizes and low powers. Much has been done in recent years to overcome these problems. However, these recent works make restrictive assumptions in terms of the number of treatments to be compared and/or the distribution of the data. This research aims to (1) propose and investigate refined …

Analysis Of 2016-17 Major League Soccer Season Data Using Poisson Regression With R, Ian D. Campbell

Undergraduate Theses and Capstone Projects

To the outside observer, soccer is chaotic with no given pattern or scheme to follow, a random conglomeration of passes and shots that go on for 90 minutes. Yet, what if there was a pattern to the chaos, or a way to describe the events that occur in the game quantifiably. Sports statistics is a critical part of baseball and a variety of other of today’s sports, but we see very little statistics and data analysis done on soccer. Of this research, there has been looks into the effect of possession time on the outcome of a game, the difference …

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 …

Modelling The Common Risk Among Equities Using A New Time Series Model, Jingjia Chu

Electronic Thesis and Dissertation Repository

A new additive structure of multivariate GARCH model is proposed where the dynamic changes of the conditional correlation between the stocks are aggregated by the common risk term. The observable sequence is divided into two parts, a common risk term and an individual risk term, both following a GARCH type structure. The conditional volatility of each stock will be the sum of these two conditional variance terms. All the conditional volatility of the stock can shoot up together because a sudden peak of the common volatility is a sign of the system shock.

We provide sufficient conditions for strict stationarity …

Effect Of Socioeconomic And Demographic Factors On Kentucky Crashes, Aaron Berry Cambron

Theses and Dissertations--Civil Engineering

The goal of this research was to examine the potential predictive ability of socioeconomic and demographic data for drivers on Kentucky crash occurrence. Identifying unique background characteristics of at-fault drivers that contribute to crash rates and crash severity may lead to improved and more specific interventions to reduce the negative impacts of motor vehicle crashes. The driver-residence zip code was used as a spatial unit to connect five years of Kentucky crash data with socioeconomic factors from the U.S. Census, such as income, employment, education, age, and others, along with terrain and vehicle age. At-fault driver crash counts, normalized over …

Making Models With Bayes, Pilar Olid

Electronic Theses, Projects, and Dissertations

Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects …

On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, Daniel Prawira

Electronic Thesis and Dissertation Repository

In family studies, we are interested in estimating the penetrance function of the event of interest in the presence of competing risks. Failure to account for competing risks may lead to bias in the estimation of the penetrance function. In this thesis, three statistical challenges are addressed: clustering, missing data, and competing risks. We proposed the cause-specific model with shared frailty and ascertainment correction to account for clustering and competing risks along with ascertainment of families into study. Multiple imputation is used to account for missing data. The simulation study showed good performance of our proposed model in estimating the …

Performance Of Imputation Algorithms On Artificially Produced Missing At Random Data, Tobias O. Oketch

Electronic Theses and Dissertations

Missing data is one of the challenges we are facing today in modeling valid statistical models. It reduces the representativeness of the data samples. Hence, population estimates, and model parameters estimated from such data are likely to be biased.

However, the missing data problem is an area under study, and alternative better statistical procedures have been presented to mitigate its shortcomings. In this paper, we review causes of missing data, and various methods of handling missing data. Our main focus is evaluating various multiple imputation (MI) methods from the multiple imputation of chained equation (MICE) package in the statistical software …

Modelling Cash Crop Growth In Tn, Spencer Weston

Chancellor’s Honors Program Projects

No abstract provided.

Network Exploration Of Correlated Multivariate Protein Data For Alzheimer's Disease Association, Matthew J. Lane

Theses

Alzheimer Disease (AD) is difficult to diagnose by using genetic testing or other traditional methods. Unlike diseases with simple genetic risk components, there exists no single marker determining as to whether someone will develop AD. Furthermore, AD is highly heterogeneous and different subgroups of individuals develop the disease due to differing factors. Traditional diagnostic methods using perceivable cognitive deficiencies are often too little too late due to the brain having suffered damage from decades of disease progression. In order to observe AD at early stages prior to the observation of cognitive deficiencies, biomarkers with greater accuracy are required. By using …

Quantifying The Effect Of The Shift In Major League Baseball, Christopher John Hawke Jr.

Senior Projects Spring 2017

Baseball is a very strategic and abstract game, but the baseball world is strangely obsessed with statistics. Modern mainstream statisticians often study offensive data, such as batting average or on-base percentage, in order to evaluate player performance. However, this project observes the game from the opposite perspective: the defensive side of the game. In hopes of analyzing the game from a more concrete perspective, countless mathemeticians - most famously, Bill James - have developed numerous statistical models based on real life data of Major League Baseball (MLB) players. Large numbers of metrics go into these models, but what this project …

A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz

Doctor of Business Administration Dissertations

At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with …

The Influence Of The Electric Supply Industry On Economic Growth In Less Developed Countries, Edward Richard Bee

Dissertations

This study measures the impact that electrical outages have on manufacturing production in 135 less developed countries using stochastic frontier analysis and data from World Bank’s Investment Climate surveys. Outages of electricity, for firms with and without backup power sources, are the most frequently cited constraint on manufacturing growth in these surveys.

Outages are shown to reduce output below the production frontier by almost five percent in Africa and by a lower percentage in South Asia, Southeast Asia and the Middle East and North Africa. Production response to outages is quadratic in form. Outages also increase labor cost, reduce exports …

Quantifying Transit Access In New York City: Formulating An Accessibility Index For Analyzing Spatial And Social Patterns Of Public Transportation, Maxwell S. Siegel

Theses and Dissertations

This paper aims to analyze accessibility within New York City’s transportation system through creating unique accessibility indices. Indices are detailed and implemented using GIS, analyzing the distribution of transit need and access. Regression analyses are performed highlighting relationships between demographics and accessibility and recommendations for transit expansion are presented.

Development In Normal Mixture And Mixture Of Experts Modeling, Meng Qi

Theses and Dissertations--Statistics

In this dissertation, first we consider the problem of testing homogeneity and order in a contaminated normal model, when the data is correlated under some known covariance structure. To address this problem, we developed a moment based homogeneity and order test, and design weights for test statistics to increase power for homogeneity test. We applied our test to microarray about Down’s syndrome. This dissertation also studies a singular Bayesian information criterion (sBIC) for a bivariate hierarchical mixture model with varying weights, and develops a new data dependent information criterion (sFLIC).We apply our model and criteria to birth- weight and gestational …

Dimension Reduction And Variable Selection, Hossein Moradi Rekabdarkolaee

Theses and Dissertations

High-dimensional data are becoming increasingly available as data collection technology advances. Over the last decade, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics, signal processing, and environmental studies. Statistical techniques such as dimension reduction and variable selection play important roles in high dimensional data analysis. Sufficient dimension reduction provides a way to find the reduced space of the original space without a parametric model. This method has been widely applied in many scientific fields such as genetics, brain imaging analysis, econometrics, environmental sciences, etc. …

Using Spatiotemporal Methods To Fill Gaps In Energy Usage Interval Data, Kristin K. Graves

Theses and Dissertations

Researchers analyzing spatiotemporal or panel data, which varies both in location and over time, often find that their data has holes or gaps. This thesis explores alternative methods for filling those gaps and also suggests a set of techniques for evaluating those gap-filling methods to determine which works best.

The Effects Of Quantitative Easing In The United States: Implications For Future Central Bank Policy Makers, Matthew Q. Rubino

Senior Honors Projects, 2010-2019

The purpose of this thesis is to examine the effects of the Federal Reserve’s recent bond buying programs, specifically Quantitative Easing 1, Quantitative Easing 2, Operation Twist (or the Fed’s Maturity Extension Program), and Quantitative Easing 3. In this study, I provide a picture of the economic landscape leading up to the deployment of the programs, an overview of quantitative easing including each program’s respective objectives, and how and why the Fed decided to implement the programs. Using empirical analysis, I measure each program’s effectiveness by applying four models including a yield curve model, an inflation model, a money supply …

Examining The Performance Of The Metropolis-Hastings Robbins-Monro Algorithm In The Estimation Of Multilevel Multidimensional Irt Models, Bozhidar M. Bashkov

Dissertations, 2014-2019

The purpose of this study was to review the challenges that exist in the estimation of complex (multidimensional) models applied to complex (multilevel) data and to examine the performance of the recently developed Metropolis-Hastings Robbins-Monro (MH-RM) algorithm (Cai, 2010a, 2010b), designed to overcome these challenges and implemented in both commercial and open-source software programs. Unlike other methods, which either rely on high-dimensional numerical integration or approximation of the entire multidimensional response surface, MH-RM makes use of Fisher’s Identity to employ stochastic imputation (i.e., data augmentation) via the Metropolis-Hastings sampler and then apply the stochastic approximation method of Robbins and Monro …

Relationship Between High School Math Course Selection And Retention Rates At Otterbein University, Lauren A. Fisher

Undergraduate Honors Thesis Projects

Binary logistic regression was used to study the relationship between high school math course selection and retention rates at Otterbein University. Graduation rates from postsecondary institutions are low in the United States and, more specifically, at Otterbein. This study is important in helping to determine what can raise retention rates, and ultimately, graduation rates. It directs focus toward high school math course selection and what should be changed before entering a post-secondary institution. Otterbein will have a better idea of what type of students to recruit and which students may be good candidates with some extra help. Recruiting is expensive, …

The Structure Of Child And Adolescent Aggression: Confirmatory Factor Analysis Of A Brief Peer Conflict Scale, Justin Russell

University of New Orleans Theses and Dissertations

The importance of simultaneous consideration of forms and functions in youth measures of aggressive behavior is well established. Competing models have presented these highly interrelated constructs as either independent (e.g., reactive or overt) or paired factors (e.g., reactive and overt). The current study examines these models in the context of assessing the viability of a new self-report measure, the Peer Conflict Scale – 20 Item Version. Confirmatory factor analyses were conducted on PCS 20 responses from 1,048 school-age youth living in the Gulf Coast region. Both models significantly improved upon one or two-factor alternatives, and demonstrated partial invariance across gender …

Impacts Of Climate Change On The Evolution Of The Electrical Grid, Melissa Ree Allen

Doctoral Dissertations

Maintaining interdependent infrastructures exposed to a changing climate requires understanding 1) the local impact on power assets; 2) how the infrastructure will evolve as the demand for infrastructure changes location and volume and; 3) what vulnerabilities are introduced by these changing infrastructure topologies. This dissertation attempts to develop a methodology that will a) downscale the climate direct effect on the infrastructure; b) allow population to redistribute in response to increasing extreme events that will increase under climate impacts; and c) project new distributions of electricity demand in the mid-21^st century.

The research was structured in three parts. The first …

Dynamic Bayesian Approaches To The Statistical Calibration Problem, Derick Lorenzo Rivers

Theses and Dissertations

The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used …