Statistics and Probability Commons^™

Application Of Isotonic Regression In Predicting Business Risk Scores, Linh T. Le, Jennifer L. Priestley

Published and Grey Literature from PhD Candidates

An isotonic regression model fits an isotonic function of the explanatory variables to estimate the expectation of the response variable. In other words, as the function increases, the estimated expectation of the response must be non-decreasing. With this characteristic, isotonic regression could be a suitable option to analyze and predict business risk scores. A current challenge of isotonic regression is the decrease of performance when the model is fitted in a large data set e.g. more than four or five dimensions. This paper attempts to apply isotonic regression models into prediction of business risk scores using a large data set …

The Association Between Osteoporosis And Early Menopause Following Hysterectomy, Mia Meeyaong-Won Botkin

Walden Dissertations and Doctoral Studies

Osteoporosis is considered to be the most adverse public health disease associated with substantial mortality among postmenopausal women. Hysterectomy, surgically induced menopause, contributes to the early onset of menopause. However, there was no evidence of an association between early menopause following hysterectomy and osteoporosis among postmenopausal women. The purpose of this quantitative study was to examine the association between demographic and behavioral factors and the prevalence of osteoporosis among hysterectomized postmenopausal women. The integrated theory of health behavior change theoretical framework guided study. Cross-sectional secondary data from the 2009-2010 National Health and Nutrition Examination Survey were used. Multiple logistic regression …

Modern Estimation Problems In Group Testing, Md Shamim Sarker

Theses and Dissertations

In the simplest form of group testing, pools are formed by compositing a fixed number of individual specimens (e.g., blood, urine, swab, etc.) and then the pools are tested for a binary characteristic, such as presence or absence of a disease. Group testing is commonly used to screen for a variety of sexually transmitted diseases in epidemiological applications where the main goal is to increase testing efficiency. In this dissertation, we study three estimation problems that are motivated by real-life applications. We propose new methods to model group testing data for both single and multiple infections. In the first problem, …

Semiparametric Joint Dynamic Modeling Of A Longitudinal Marker, Recurrent Competing Risks, And A Terminal Event, Piaomu Liu

Theses and Dissertations

The joint modeling framework has found extensive applications in cancer and other biomedical research. For example, recent initiatives and developments in precision medicine call for appropriate prognostic tools to assist individualized or personalized approaches in cancer diagnosis and treatment. Data generated by clinical trials and medical research often include correlated longitudinal marker measurements and time- to-event information, which are possibly a recurrent event, competing risks, and a survival outcome. Primary interests of joint modeling include the association between the longitudinal marker measurements and time-to-event data, as well as predictions of survival probabilities of new observational units from the same population. …

Spatio-Temporal Analysis Of The Occupational Fatal Victimization Of Law Enforcement Officers In The Us, Xueyi Xing

Theses and Dissertations

The models with constant coefficients of the covariates across space and time are commonly used in spatio-temporal analyses. However, the associations between risk factors and the outcome could have locally differential temporal trends in many cases. In this study, a Bayesian latent cluster modeling strategy is employed to identify potential spatial clusters in which locally specific sets of temporally varying coefficients of covariates are allowed. A state-level panel data of police officers occupational fatal victimization for the years 1979-2010 is used. To accommodate overdisperson and excess zeros, a negative binomial model and zero-inflated Poisson/negative binomial models are also utilized. A …

Regression Models For Count Data Based On The Double Poisson Distribution, Rebecca Wardrop

Theses and Dissertations

This paper explores the double Poisson distribution. The probability mass function and the difficulties associated with derivative-based optimization for this distribution are discussed. Stata software developed for estimation of double Poisson regression is detailed. Simulations are used to test the software. Data which are over-, under-, and equidispersed relative to the Poisson are generated and the software is utilized to estimate a regression model, a zero-inflated model, and a marginalized zero-inflated model all based on the double Poisson distribution. The estimated power of the test for φ = 1 for the double Poisson models are compared to the power of …

Semiparametric Estimation Methods For Complex Accelerated Failure Time Model, Yinding Wang

Theses and Dissertations

The proportional hazards (PH) model and the accelerated failure time (AFT) model are the two most popular survival models in fitting the right-censored data. The AFT model is a useful alternative to the PH model, particularly when the PH assumption is not satisfied. Usually, the linear association is assumed with logarithm of survival time in the AFT model. However, the nonlinear association may exist in practice. The first project aims to handle the nonlinear component in the AFT model, which is called the semiparametric additive partial accelerated failure time (AP-AFT) model. Two estimation methods based on the rank-smooth method and …

Frailty Probit Models For Clustered Interval-Censored Failure Time Data, Haifeng Wu

Theses and Dissertations

Survival analysis is an important branch of statistics that deals with time to event data or survival data. An important feature of such data is that the survival time of interest is usually not completely known but is censored due to the design of the study or an early dropout. In this dissertation we focus on studying clustered interval-censored data, a special type of survival data. Interval-censored data arise in many epidemiological, social science, and medical studies, in which subjects are examined at periodical follow-up visits. The survival (or failure) time of interest is never exactly observed but is known …

A Weighted Gene Co-Expression Network Analysis For Streptococcus Sanguinis Microarray Experiments, Erik C. Dvergsten

Theses and Dissertations

Streptococcus sanguinis is a gram-positive, non-motile bacterium native to human mouths. It is the primary cause of endocarditis and is also responsible for tooth decay. Two-component systems (TCSs) are commonly found in bacteria. In response to environmental signals, TCSs may regulate the expression of virulence factor genes.

Gene co-expression networks are exploratory tools used to analyze system-level gene functionality. A gene co-expression network consists of gene expression profiles represented as nodes and gene connections, which occur if two genes are significantly co-expressed. An adjacency function transforms the similarity matrix containing co-expression similarities into the adjacency matrix containing connection strengths. Gene …

Provision Of Hospital-Based Palliative Care And The Impact On Organizational And Patient Outcomes, Marisa L. Roczen

Theses and Dissertations

Hospital-based palliative care services aim to streamline medical care for patients with chronic and potentially life-limiting illnesses by focusing on individual patient needs, efficient use of hospital resources, and providing guidance for patients, patients’ families and clinical providers toward making optimal decisions concerning a patient’s care. This study examined the nature of palliative care provision in U.S. hospitals and its impact on selected organizational and patient outcomes, including hospital costs, length of stay, in-hospital mortality, and transfer to hospice. Hospital costs and length of stay are viewed as important economic indicators. Specifically, lower hospital costs may increase a hospital’s profit …

In-Shoe Plantar Pressure System To Investigate Ground Reaction Force Using Android Platform, Ahmed A. Mostfa

Theses and Dissertations

Human footwear is not yet designed to optimally relieve pressure on the heel of the foot. Proper foot pressure assessment requires personal training and measurements by specialized machinery. This research aims to investigate and hypothesize about Preferred Transition Speed (PTS) and to classify the gait phase of explicit variances in walking patterns between different subjects. An in-shoe wearable pressure system using Android application was developed to investigate walking patterns and collect data on Activities of Daily Living (ADL). In-shoe circuitry used Flexi-Force A201 sensors placed at three major areas: heel contact, 1st metatarsal, and 5th metatarsal with a PIC16F688 microcontroller …

Selecting Spatial Scale Of Area-Level Covariates In Regression Models, Lauren Grant

Theses and Dissertations

Studies have found that the level of association between an area-level covariate and an outcome can vary depending on the spatial scale (SS) of a particular covariate. However, covariates used in regression models are customarily modeled at the same spatial unit. In this dissertation, we developed four SS model selection algorithms that select the best spatial scale for each area-level covariate. The SS forward stepwise, SS incremental forward stagewise, SS least angle regression (LARS), and SS lasso algorithms allow for the selection of different area-level covariates at different spatial scales, while constraining each covariate to enter at most one spatial …

Meta-Analytic Estimation Techniques For Non-Convergent Repeated-Measure Clustered Data, Aobo Wang

Theses and Dissertations

Clustered data often feature nested structures and repeated measures. If coupled with binary outcomes and large samples (>10,000), this complexity can lead to non-convergence problems for the desired model especially if random effects are used to account for the clustering. One way to bypass the convergence problem is to split the dataset into small enough sub-samples for which the desired model convergences, and then recombine results from those sub-samples through meta-analysis. We consider two ways to generate sub-samples: the K independent samples approach where the data are split into k mutually-exclusive sub-samples, and the cluster-based approach where naturally existing …

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. …

Macroconstants Of Development: A New Benchmark For The Strategic Development Of Advanced Countries And Firms, Andrey V. Bystrov, Vyacheslav N. Yusim, Tamilla Curtis

Publications

This research proposed a new indicator of countries’ development called “macroconstants of development”. The literature review indicates that the concept of "macroconstants of development" is not used at the moment in neither the theory nor the practice of industrial policy. Research of longitudinal data of total GDP, GDP per capita and their derivatives for most countries of the world was conducted. An analysis of statistical information has been done by employing econometric analyses.

Based on the analysis of the statistical data, which characterizes the development of large, technologically advanced countries in ordinary conditions, it was identified that the average acceleration …

On The Double Chain Ladder For Reserve Estimation With Bootstrap Applications, Larissa Schoepf

Masters Theses

"To avoid insolvency, insurance companies must have enough reserves to fulfill their present and future commitment-refer to in this thesis as outstanding claims towards policyholders. This entails having an accurate and reliable estimate of funds necessary to cover those claims as they are presented. One of the major techniques used by practitioners and researchers is the single chain ladder method. However, though most popular and widely used, the method does not offer a good understanding of the distributional properties of the way claims evolve. In a series of recent papers, researchers have focused on two potential components of outstanding claims, …

Missing Data In Clinical Trial: A Critical Look At The Proportionality Of Mnar And Mar Assumptions For Multiple Imputation, Theophile B. Dipita

College of Graduate Studies: Theses & Dissertations

Randomized control trial is a gold standard of research studies. Randomization helps reduce bias and infer causality. One constraint of these studies is that it depends on participants to obtain the desired data. Whatever the researcher can do, there is a possibility to end up with incomplete data. The problem is more relevant in clinical trials when missing data can be related to the condition under study. The benefits of randomization is compromised by missing data. Multiple imputation is a valid method of treating missing data under the assumption of MAR. Unfortunately this is an unverified assumptions. Current practice advise …

Impairment Of Continuous Insulin Delivery Therapy And Analysis From Graeco-Latin Square Design Model, Norou Diawara, Ayodeji Demuren, Eric Gyuricsko

Mathematics & Statistics Faculty Publications

The desire to deliver measured amount of insulin continuously to patients with type I diabetes, for glycemic control, has attracted a lot of attention. Continuous subcutaneous insulin infusion has seen some success in recent years. However, occlusion of insulin delivery may prevent the patient from receiving the prescribed dosage, with adverse consequence. An in vitro study of insulin delivery is performed, using different insulin pumps, insulin analogs and operating conditions. The aim is to identify incidences of occlusion due to bubble formation in the infusion line. A detailed statistical analysis was performed on the data collected to determine any significant …

Data Analytics On Consumer Behavior In Omni-Channel Retail Banking, Card And Payment Services, Geng Dan

Research Collection School Of Computing and Information Systems

Innovations in financial services have created challenges for banks that Information Systems (IS) research can address. My interests involve transaction cost theory, substitution and complementarity theory, and consumer informedness theory to understand consumer behavior and firm performance in the omni-channel world of digital banking. At a high level, my research inquiry asks: How can financial institutions take advantage of the deep insights that data analytics and management science modeling create on consumer behavior and channel management decision-making? And how can changes in payments and services in retail banking be understood in spatial and temporal terms? I am working on three …

Sample Size Estimation For Genomics Experiments With Dependent End Points, Desmond Koomson

Open Access Theses & Dissertations

In typical genomics studies involving numerous association tests of gene mutations with a disease, error rate control via multiplicity adjustment is paramount because even if all genes were to be non-differentially associated, we would still make some false positives. Many methods exist that incorporate the control of multiplicity for normally distributed endpoints in sample size estimation, but none addresses the issue for non-normally correlated endpoints.

One common practice in the literature is to assume an equal correlation among all differentially associated or expressed genes, thereby using the generalized binomial or beta-binomial model to compute the comparison-wise power of detecting these …

Development Of Efficient Simultaneous Confidence Bounds For Linear Mixed Models With Applications In Alcohol Research, Emmanuel Joseph Sequeira

Open Access Theses & Dissertations

Multiplicity corrections are necessary to ensure the accuracy of conclusions made in studies that carry out multiple inferences simultaneously. This Thesis uses the methodology derived by Hunter and Worsley to obtain improved simultaneous confidence bounds (SCBs) that are less conservative than the highly used Bonferroni SCBs, for studies using linear mixed modeling. Empirical coverage rates were obtained for data that was generated using simulations, to compare the accuracy of the Hunter-Worsley SCBs with that of the Bonferroni SCBs. The bounds were also applied to data in the field of alcohol research, where comparisons were made to determine the moderating effect …

Pre-Tuned Principal Component Regression And Several Variants, Pei Wang

Open Access Theses & Dissertations

The regression coecient estimates from ordinary least squares (OLS) have a low probability of being close to the real value when there is a multicollinearity problem in the design matrix. In order to combat this problem, many regularized methods have been introduced. Principal components regression (PCR) is an important analysis tool for dealing with multicollinearity and high-dimensionality. In conventional PCR, the rst step is to change the original predictors to orthogonal principal components (PC's) by a linear transformation. These PC's correspond to the eigenvalues which are sorted in a decreasing order. The next step is to regress the response on …

Bayesian Parameter Estimation For The Birnbaum-Saunders Distribution And Its Extension, Tun Lee Ng

Open Access Theses & Dissertations

We utilize the Bayesian approach to estimate the parameters of the Birnbaum-Saunders (BS) distribution devised by Birnbaum and Saunders (1969a), as well as the Generalized Birnbaum-Saunders (GBS) distribution obtained by Owen (2006), in the presence of random right censored data. We also derive the classical MLE expressions for the observed Information matrix of the GBS distribution, in order to illustrate the fact that no closed form expressions are available for the MLE, and numerical approximations are required to obtain the point estimates and asymptotic confidence intervals. Where Bayesian approach is concerned, new sets of priors are considered based on the …

A New Test For The Mean Vector In High Dimensional Setting, Behzad Aalipur Hafshejani

Open Access Theses & Dissertations

Traditional statistical data analysis mostly includes methods and techniques to deal with problems in which there are many observations but a few variables. Nonetheless, the current inclination is toward more observations but also, toward more variables. Today's observations gathered on individuals are images, curves, or even movies. Unfortunately many traditional methods do not work well in high dimensional settings. As an example Hotelling's test which is well known and widely used in the literature does not work when it comes to high dimensional problems. Consequently statisticians are making an effort to find remedies or new approaches to multivariate mean testing. …

An Online Statistics Course From Faculty And Students' Perspectives: A Case Study, Ruth Best

Walden Dissertations and Doctoral Studies

Faculty at a private college in the northeastern United States found students lacked prerequisite mathematical skills and were unable to transfer quantitative reasoning skills to upper level business courses. Guided by Mezirow's transformative learning theory and Knowles' approach to self-directed learning, this study examined how undergraduate students learn statistics online. The purpose of this qualitative embedded case study was to examine faculty and students' perspectives about the online statistics course design and delivery while exploring possible barriers to students' learning. Data collection occurred by review of course documents and the learning management system. Archival data generated questions for semistructured interviews …

Anatomy, Implant Selection And Placement Influence Spine Mechanics Associated With Total Disc Replacement, Justin F.M. Hollenbeck

Electronic Theses and Dissertations

Through aging and injury, the intervertebral disc of the lumbar spine can undergo degeneration, leading to collapse of the vertebrae and low back pain, a symptom that affects half the adult population in any given year. In an effort to reduce low back pain, total disc replacement treatment removes the degenerated disc, restores natural height and lordosis of the segment, and preserves motion at the joint. Patient anatomy, implant selection, and implant placement play significant roles in a patient's outcomes after total disc replacement surgery. Thus, the objective of the work presented in this thesis was to develop a suite …

Collective Action And Decision Making: An Analysis Of Economic Modeling And Environmental Free-Riding, Thomas Miller 16

Honor Scholar Theses

No abstract provided.

A Data Science Course For Undergraduates: Thinking With Data, Benjamin Baumer

Mathematics Sciences: Faculty Publications

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be nontraditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students to a variety of techniques to analyze small, neat, and clean datasets. However, whether they pursue more formal training in statistics or not, many of these students will end up …

System-Wide Prediction Of General, All-Cause, Preventable Hospital Readmissions, Ken Musselman, Brandon Pope, Steve Witz, Zhiyi Tian, Lingsong Zhang, Linda Leon, Ann Davis

RCHE Publications

Existing studies of hospital readmissions typically focus on specific diagnoses, age groups, discharge dispositions, payer classes, or hospitals, and often use small samples. It is not clear how predictive models generated from such studies generalize across diseases, hospitals, or time periods. In this study, a logistic regression model of readmission risk within 30 days based on hospital administrative data was constructed and validated across hospitals and time periods. The hospitals included both general and specialty hospitals such as long-term care, women’s, and children’s hospitals. The administrative data included information on patient’s demographics, diagnoses, procedures, and discharge disposition. Derivation and validation …

Statistical Handling Of Medical Data - An Ethical Perspective, Ajay Kumar Bansal Dr

COBRA Preprint Series

Medical Science is a delicate subject and the clinical data generated from the medical trials must be reliable and of good quality. Not only the quality of generated data is important, but the management is also crucial and is to be handled very carefully. In this paper, the ethical aspect of statistical handling of such data is discussed.

Every profession has some set of norms to follow to achieve its objectives. These norms are called professional ethics which shows the essence of human behaviour. Same way, the field of medical research is expected to follow ethical norms, to obtain reliable …