Statistical Models Commons^™

Study On Innovation Networks And Its Spillover Effect Of China’S New Energy Automobile Industry, Zhifei Xiong, Wenzhong Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The network spillover effect of knowledge has been playing an increasingly significant role in the development of industrial innovation. The urban cooperation matrix of China’s new energy automobile industry is built based on new energy automobile patent data, and the structure and evolution process of China’s new energy automobile industry are depicted. On this basis, the spatial Dubin model (SDM) is used to calculate the network spillover effect, and its results are compared with the results of spillover effect based on the relationship of spatial contiguity and distance of cities. The results show that the innovation activities of China’s new …

Towards Structured Planning And Learning At The State Fisheries Agency Scale, Caleb A. Aldridge

Theses and Dissertations

Inland recreational fisheries has grown philosophically and scientifically to consider economic and sociopolitical aspects (non-biological) in addition to the biological. However, integrating biological and non-biological aspects of inland fisheries has been challenging. Thus, an opportunity exists to develop approaches and tools which operationalize planning and decision-making processes which include biological and non-biological aspects of a fishery. This dissertation expands the idea that a core set of goals and objectives is shared among and within inland fisheries agencies; that many routine operations of inland fisheries managers can be regimented or standardized; and the novel concept that current information and operations can …

Larval Ecology Of Atlantic Bluefin Tuna (Thunnus Thynnus): New Insights From Otolith Microstructure, Biotic, And Abiotic Analyses From The Gulf Of Mexico And Mediterranean Sea, Estrella Malca

All HCAS Student Capstones, Theses, and Dissertations

Atlantic bluefin tuna (ABT), Thunnus thynnus, spawn in the Gulf of Mexico (GoM) and the Mediterranean Sea (MED). Spawning occurs within narrow temporal and environmental parameters. Efforts to characterize growth of ABT in wild conditions revealed a wide range of growth variability during the early life stages. This series of studies examined potential biotic and abiotic influences of larval growth from seven ABT cohorts, and identified several key drivers of growth for this commercially valuable species. A detailed investigation of larval dynamics using otolith microstructure was conducted as follows. First, companion growth curves and stable isotope analysis from the same …

Bayesian Methods For Graphical Models With Neighborhood Selection., Sagnik Bhadury

Electronic Theses and Dissertations

Graphical models determine associations between variables through the notion of conditional independence. Gaussian graphical models are a widely used class of such models, where the relationships are formalized by non-null entries of the precision matrix. However, in high-dimensional cases, covariance estimates are typically unstable. Moreover, it is natural to expect only a few significant associations to be present in many realistic applications. This necessitates the injection of sparsity techniques into the estimation method. Classical frequentist methods, like GLASSO, use penalization techniques for this purpose. Fully Bayesian methods, on the contrary, are slow because they require iteratively sampling over a quadratic …

Infrastructure Development For Personalized Risk Prediction To Reduce Cardiovascular Disease In Childhood Cancer Survivors, Suman Shrestha

Dissertations and Theses (Open Access)

Although childhood cancer survivors have lengthy life expectancies, they run the risk of experiencing long-term health issues as a result of their treatment. The most frequent non-cancerous cause of morbidity and mortality for these survivors is cardiac disease. Radiation therapy (RT) has been linked in numerous cohort studies to a higher chance of developing a late cardiac disease in these survivors, and this risk rises with higher mean heart doses and increased RT exposure to larger cardiac volumes. Since, the heart is a heterogeneous organ made up of several distinct substructures, RT dose received by the entire heart does not …

Statistical Methods For Modern Threats, Brandon Lumsden

All Dissertations

More than ever before, technology is evolving at a rapid pace across the broad spectrum of biological sciences. As data collection becomes more precise, efficient, and standardized, a demand for appropriate statistical modeling grows as well. Throughout this dissertation, we examine a variety of new age data arising from modern technology of the 21st century. We begin by employing a suite of existing statistical techniques to address research questions surrounding three medical conditions presenting in public health sciences. Here we describe the techniques used, including generalized linear models and longitudinal models, and we summarize the significant associations identified between research …

Learning Graphical Models Of Multivariate Functional Data With Applications To Neuroimaging, Jiajing Niu

All Dissertations

This dissertation investigates the functional graphical models that infer the functional connectivity based on neuroimaging data, which is noisy, high dimensional and has limited samples. The dissertation provides two recipes to infer the functional graphical model: 1) a fully Bayesian framework 2) an end-to-end deep model.

We first propose a fully Bayesian regularization scheme to estimate functional graphical models. We consider a direct Bayesian analog of the functional graphical lasso proposed by Qiao et al. (2019).. We then propose a regularization strategy via the graphical horseshoe. We compare both Bayesian approaches to the frequentist functional graphical lasso, and compare the …

Fiscal And Monetary Policy Interactions In A Developing Economy: A Dsge-Based Evidence From Nigeria, Queen E. Oye, Philip O. Alege

CBN Journal of Applied Statistics (JAS)

This study characterizes the nature of fiscal-monetary interaction in Nigeria and gauges its macroeconomic effects by estimating a New Keynesian Dynamic Stochastic General Equilibrium (NK DSGE) model. Two policy simulations were also conducted. The first experiment considers the desirable active-passive policy mix while the second experiment ranks alternative monetary policy rules among the differing objectives of price, output and exchange rate stabilization. The study finds that fiscal and monetary policies interact as complements in an active monetary and passive fiscal policy mix over the sample period. The result from the first policy simulation reveals that the active monetary and passive …

Market Risk Factors And Stock Returns In The Nigerian Bourse, Omorose A. Ogiemudia, Osagie Osifo, Igbinovia L. Eghosa

CBN Journal of Applied Statistics (JAS)

This study examines the link between market risk and equity return in Nigeria between 1980 to 2019. It employs the vector error correction model (VECM) to determine the short run dynamics and long run effect of market risk factors on stock return. The findings revealed that a dynamic relationship exists between market risk factors and stock returns in Nigeria. Also, exchange rate risk and oil price risks have significant influence on stock return, while inflation and interest rate risk, and political instability risks have a non-significant impact on stock return. Finally, a unidirectional relationship was detected between interest rate, oil …

Evaluation Of Circular Logistic Regression Models With Asymmetrical Link Functions, Feridun Tasdan

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Estimating R0 For Dengue Emergence In Central Argentina Using Statistical Models, Sahil Chindal

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Functional Data Analysis Of Covid-19, Nichole L. Fluke

Mathematics & Statistics ETDs

This thesis deals with Functional Data Analysis (FDA) on COVID data. The Data involves counts for new COVID cases, hospitalized COVID patients, and new COVID deaths. The data used is for all the states and regions in the United States. The data starts in March 1^st, 2020 and goes through March 31^st, 2021. The FDA smooths the data and looks to see if there are similarities or differences between the states and regions in the data. The data also shows which states and regions stand out from the others and which ones are similar. Also shown …

Bayesian Estimation Of The Intensity Function Of A Non-Homogeneous Poisson Process, James Jensen

Theses

In this paper we explore Bayesian inference and its application to the problem of estimating the intensity function of a non-homogeneous Poisson process. These processes model the behavior of phenomena in which one or more events, known as arrivals, occur independently of one another over a certain period of time. We are concerned with the number of events occurring during particular time intervals across several realizations of the process. We show that given sufficient data, we are able to construct a piecewise-constant function which accurately estimates the mean rates on particular intervals. Further, we show that as we reduce these …

An Attempt To Develop A Measurement Tool For Interpretation Performance Of Tourist Guides, Gizem Capar, Dilek Atci

University of South Florida (USF) M3 Publishing

The search for different experiences in touristic visits brings the necessity of differentiating the tours for tour guides with. Interpretation lies at the heart of this differentiation. This research aims to examine the structure of interpretation performance of tour guides empirically within the framework of E.R.O.T/T.O.R.E model. For this purpose, in line with the literature firstly conceptual structure of interpretation performance and interpretative guiding was determined, then expert opinion was sought with the expression pool consisting of draft statements. After expertising process, the measurement tool was first applied on a sample of 191 participants. For preliminary analysis the performance of …

Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti

SMU Data Science Review

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …

Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan

SMU Data Science Review

Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …

Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler

SMU Data Science Review

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three …

Understanding Consumers' Use Experience On Electrically Heated Jacket: A Study On Online Review Using Topic Modeling, Md Nakib-Ul Hasan

LSU Doctoral Dissertations

The demand for heated jackets is anticipated to be fuelled by frequent temperature drops, severe winter weather, and increasing outdoor activities. Electrically heated jackets (EHJ) are primarily marketed through online distribution channels and expansion of online sales channels is expected to boost the global market. Consumers are increasingly relying on online reviews from other consumers to help them decide what to buy. Businesses also actively monitor and manage their online reviews to build trust in their brand and make it more likely that customers will buy. Traditional approaches for assessing customer behavior, such as market research surveys and focus groups, …

Between “Breaking” And “Building”: The Bridge Theory Of Research Evaluation, Fang Xu, Xiaoxuan Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

How to build "new standards" after breaking "Siwei" is a hot and difficult issue in the current reform of research evaluation, which urgently needs good theoretical and methodological support. In this context, this study puts forward the BRIDGE theory of research evaluation of scientific researchers' achievements, which is to integrate the reasonable elements in the quantitative evaluation based on SCI papers into the "new standard" based on peer review, so as to build a bridge between quantitative analysis and qualitative evaluation. The practical application of BRIDGE theory is expressed as "Six Steps", in which the second step "Recode" and the …

Exploration In Mental Performance For Division 1 Sec College Football Student Athletes, Alex Burgdorf

Department of Occupational Therapy Entry-Level Capstone Projects

The stigma surrounding mental health in sports has made intervention difficult. “There is a need for various actors to provide more effective strategies to overcome the stigma that surrounds mental illness, increase mental health literacy in the athlete/coach community, and address athlete-specific barriers to seeking treatment for mental illness” (Castadelli-Maia et.al 2019). The athletes in the football program at the University of Tennessee face more pressure today than ever in history. They have their class schedule, practice and training every day, and meetings with their position coaches. Now, with the introduction of name, image, and likeness (NIL) allowing players to …

Abm Simulation Model Of A Pandemic For Optimizing Vaccination Strategy, Gibeom Park

Theses and Dissertations

This study presents a process-oriented hybrid model for individuals' immune responses and interactions involving vaccination to describe the trend of contagious disease and estimate the future societal cost. The model considers "recovery" as a non-absorbing state and incorporates various infection stage states including two symptomatic states. To model contagiousness to be consistent with the current pandemic and include that the spread of a disease depends on the mobility of people, we developed an Agent-Based Simulator that fitted to the particular model used in this study and can test various what-if scenarios. We improved the simulator considerably by appying data structures …

Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model, Jaehyeon Yun

Statistical Science Theses and Dissertations

Alternating recurrent events data arise commonly in health research; examples include hospital admissions and discharges of diabetes patients; exacerbations and remissions of chronic bronchitis; and quitting and restarting smoking. Recent work has involved formulating and estimating joint models for the recurrent event times considering non-negligible event durations. However, prediction models for transition between recurrent events are lacking. We consider the development and evaluation of methods for predicting future events within these models. Specifically, we propose a tool for dynamically predicting transition between alternating recurrent events in real time. Under a flexible joint frailty model, we derive the predictive probability of …

Advanced High Dimensional Regression Techniques, Yuan Yang

All Dissertations

This dissertation focuses on developing high dimensional regression techniques to analyze large scale data using both Bayesian and frequentist approaches, motivated by data sets from various disciplines, such as public health and genetics. More specifically, Chapters 2 and Chapter 4 take a Bayesian approach to achieve modeling and parameter estimation simultaneously while Chapter 3 takes a frequentist approach. The main aspects of these techniques are that they perform variable selection and parameter estimation simultaneously, while also being easily adaptable to large-scale data. In particular, by embedding a logistic model into traditional spike and slab framework and selecting of proper prior …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Statistical Methods For Personalized Treatment Selection And Survival Data Analysis Based On Observational Data With High-Dimensional Covariates., Don Ramesh Dinendra Sudaraka Tholkage

Electronic Theses and Dissertations

Due to the wide availability of functional data from multiple disciplines, the studies of functional data analysis have become popular in the recent literature. However, the related development in censored survival data has been relatively sparse. In Chapter 2, we consider the problem of analyzing time-to-event data in the presence of functional predictors. We develop a conditional generalized Kaplan Meier (KM) estimator that incorporates functional predictors using kernel weights and rigorously establishes its asymptotic properties. In addition, we propose to select the optimal bandwidth based on a time-dependent Brier score. We then carry out extensive numerical studies to examine the …

Statistical Extensions Of Multi-Task Learning With Semiparametric Methods And Task Diagnostics, Nikolay Miller

Mathematics & Statistics ETDs

In this dissertation, I propose new approaches to multi-task learning, inspired by statistical model diagnostics and semiparametric and additive modeling. The newly designed additive multi-task model framework allows for flexible estimation of multi-task parametric and nonparametric effects by using an extension of the backfitting algorithm. Further, I propose new methods for statistical task diagnostics, which allow for the identification and remedy of outlier tasks, based on task-specific performance metrics and their empirical distributions. I perform a deep examination of the well-established multi-task kernel method and achieve theoretical and experimental contributions. Lastly, I propose a two-step modeling approach to multi-task modeling, …

Applications Of Machine Learning Algorithms In Materials Science And Bioinformatics, Mohammed Quazi

Mathematics & Statistics ETDs

The piezoelectric response has been a measure of interest in density functional theory (DFT) for micro-electromechanical systems (MEMS) since the inception of MEMS technology. Piezoelectric-based MEMS devices find wide applications in automobiles, mobile phones, healthcare devices, and silicon chips for computers, to name a few. Piezoelectric properties of doped aluminum nitride (AlN) have been under investigation in materials science for piezoelectric thin films because of its wide range of device applicability. In this research using rigorous DFT calculations, high throughput ab-initio simulations for 23 AlN alloys are generated.

This research is the first to report strong enhancements of piezoelectric properties …

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

Adjusting Community Survey Data Benchmarks For External Factors, Allen Miller, Nicole M. Norelli, Robert Slater, Mingyang N. Yu

SMU Data Science Review

Abstract. Using U.S. resident survey data from the National Community Survey in combination with public data from the U.S. Census and additional sources, a Voting Regressor Model was developed to establish fair benchmark values for city performance. These benchmarks were adjusted for characteristics the city cannot easily influence that contribute to confidence in local government, such as population size, demographics, and income. This adjustment allows for a more meaningful comparison and interpretation of survey results among individual cities. Methods explored for the benchmark adjustment included cluster analysis, anomaly detection, and a variety of regression techniques, including random forest, ridge, decision …

Effect Of Monetary Policy Rate On Market Interest Rates In Nigeria: A Threshold And Nardl Approach, Oluwafemi E. Awopegba, Joseph O. Afolabi, Lydia T. Adeoye, Godwin O. Akpokodje

CBN Journal of Applied Statistics (JAS)

This study examines the effect of monetary policy rate (MPR) on market interest rates in Nigeria. For parsimony, we develop two indexes called the short-term interest rate (SINT) and Lending interest rate (LINT) to represent deposit and lending rates respectively. The nonlinear autoregressive distributed lag (NARDL) and threshold regression models are adopted. The study uses monthly data from 2002:M1 to 2019:M12. The results of the threshold regression model indicate that the degree of the effect of MPR on SINT and LINT above the estimated threshold of 11 and 13 percent respectively is greater and significant than if MPR were to …