Applied Statistics Commons^™

Longitudinal Sport Science Implementation In American Collegiate Men’S Basketball, Jason Stone

Graduate Theses, Dissertations, and Problem Reports (ETD)

The expanding opportunities to implement sport science frameworks in elite-level basketball environments coincide with the sport’s increasing global prominence. Concomitant to these opportunities is the continual growth of the sport technology market (e.g., wearables, force plates) and computational power (e.g., data management tools, coding capabilities), which yields solutions and challenges for both athletes and practitioners. Due to the rapid influx of new sport technologies in high performance environments, particularly American Collegiate Men’s Basketball, more formal and ecologically valid research on how to effectively utilize data derived from them, particularly over long periods of time (i.e., multiple seasons) is needed. To …

Network Intrusion Detection Using Deep Reinforcement Learning, Hamed T. Sanusi

College of Graduate Studies: Theses & Dissertations

This thesis delves into cybersecurity by applying Deep Reinforcement(DRL) Learning in network intrusion detection. One advantage of DRL is the ability to adapt to changing network conditions and evolving attack methods, making it a promising solution for addressing the challenges involved in intrusion detection. The thesis will also discuss the obstacles and benefits of using Classification methods for network intrusion detection and the need for high-quality training data. To train and test our proposed method, the NSL-KDD dataset was used and then adjusted by converting it from a multi-classification to a binary classification, achieved by joining all attacks into one. …

Poisson Regression Model With Application To Wastewater Surveillance Under A Threshold Linear Mixed Model For Covid-19 Sensitivity Rates, Norou Diawara, Hueiwang Anna Jeng, Kyle Curtis, Raul Gonzalez, Nancy Welch, Cynthia Jackson, Rekha Singh, David Jurgens, Sasanka Adikari, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

A Threshold Linear Mixed Model (TLMM) has been developed to identify specific thresholds based on wastewater SARS-CoV-2 viral concentrations, which reflect COVID-19 cases. The thresholds can guide decisions regarding public health responses and prevention measures. To assess the practical application of TLMM, a simple simulation was conducted using a sample size of 100 and 500 replications. The simulation allowed for comparing parameter estimators by assessing bias and standard deviation and the root of the mean square error. The model and estimation procedures were applied to reported wastewater and clinic data to test its application for real-world scenarios. Our results demonstrated …

Local Or Import? A Compositional Analysis Of Aztec Ritual Ceramics In The Tuxtlas Frontier, Veracruz, Mexico, Matthew T. Meyer

Murray State Theses and Dissertations

At the time of Spanish Contact in the early 16th Century the western Tuxtlas region formed part of the Aztec imperial frontier in the southern Gulf lowlands. The most apparent material manifestation of this imperial connection was Aztec-style Texcoco-Molded Censers, recovered primarily from sites that served local centralizing functions. While rare, these symbols may provide valuable information on the dynamics of frontier politics and the relations between this region and the distant core to which they were sending tax payments. Initial consideration of this adopted imperial style implies political linkages, but the mechanisms of introduction, knowledge transmission, imperial versus local …

Hamilton Cycles In Bidirected Complete Graphs, Arthur Busch, Mohammed A. Mutar, Daniel Slilaty

Mathematics and Statistics Faculty Publications

Zaslavsky observed that the topics of directed cycles in directed graphs and alternating cycles in edge 2-colored graphs have a common generalization in the study of coherent cycles in bidirected graphs. There are classical theorems by Camion, Harary and Moser, Häggkvist and Manoussakis, and Saad which relate strong connectivity and Hamiltonicity in directed "complete" graphs and edge 2-colored "complete" graphs. We prove two analogues to these theorems for bidirected "complete" signed graphs.

Application Of Distributed Fiber-Optic Sensing For Pressure Predictions And Multiphase Flow Characterization, Gerald Kelechi Ekechukwu

LSU Doctoral Dissertations

In the oil and gas industry, distributed fiber optics sensing (DFOS) has the potential to revolutionize well and reservoir surveillance applications. Using fiber optic sensors is becoming increasingly common because of its chemically passive and non-magnetic interference properties, the possibility of flexible installations that could be behind the casing, on the tubing, or run on wireline, as well as the potential for densely distributed measurements along the entire length of the fiber. The main objectives of my research are to develop and demonstrate novel signal processing and machine learning computational techniques and workflows on DFOS data for a variety of …

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 …

(R1899) Asymptotic Normality Of The Conditional Hazard Function In The Local Linear Estimation Under Functional Mixing Data, Amina Goutal, Boubaker Mechab, Omar Fetitah, Torkia Merouan

Applications and Applied Mathematics: An International Journal (AAM)

In this study, we are interested in using the local linear technique to estimate the conditional hazard function for functional dependent data where the scalar response is conditioned by a functional random variable. The asymptotic normality of this constructed estimator is demonstrated under some extreme conditions. Our estimator’s performance is demonstrated through simulations.

(R2024) A New Weighted Poisson Distribution For Over- And Under-Dispersion Situations, Michel Koukouatikissa Diafouka, Gelin Chedly Louzayadio, Rodnellin Onéime Malouata

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, we propose a four-parameter weighted Poisson distribution that includes and generalizes the weighted Poisson distribution proposed by Castillo and Pérez-Casany and the Conway- Maxwell-Poisson distribution, as well as other well-known distributions. It is a distribution that is a member of the exponential family and is an exponential combination formulation between the weighted Poisson distribution proposed by Castillo and Pérez-Casany and the Conway-Maxwell- Poisson distribution. This new distribution with an additional parameter of dispersion is more flexible, and the Fisher dispersion index can be greater than, equal to, or less than one. This last property allows it to …

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 …

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 …

Natural Language Processing For Disaster Tweets, Akinyemi D. Apampa, Nan Li

Publications and Research

Our goal is to establish an automatic model that identifies which tweets are about natural disasters based on the content of the tweets. Our method is to construct a decision tree based on keyword searching. We will construct the model using 7,645 tweets and test our model on 3,465 tweets as an assessment of the performance.

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 …

Estimation Of Disaggregated Import Demand Functions For Nigeria, Chekwube V. Madichie, Uche C. Nwogwugwu, Franklin N. Ngwu, Olisaemeka D. Mauka

CBN Journal of Applied Statistics (JAS)

This paper estimates disaggregated import demand function for Nigeria using annual data from 1970 to 2019. The study employs the Zivot-Andrews unit root and Gregory-Hansen cointegration tests to account for the role of structural breaks and the error correction mechanism for shortrun analysis, respectively. The results show that household consumption, industrial output and domestic investment are the major determinants of import demand for consumer, intermediate and investment goods, respectively. Furthermore, the import demand for investment goods is not sensitive to variations in relative prices. However, relative prices is negative and significant to import demand for consumer and intermediate goods. Exchange …

Size And Determinants Of The Shadow Economy In Nigeria: Evidence From A Monetary Approach, Tari M. Karimo, Mohammed M. Tumala, Ibrahim U, Wambai

CBN Journal of Applied Statistics (JAS)

Thiis study investigates the size and determinants of the shadow economy in Nigeria. It adopts an aggregation approach within the monetary framework and utilises the ARDL estimation technique to analyse quarterly data from 2010 Q1 to 2019 Q4. On average, the results suggest that the quarterly size of the shadow economy is about 55 per cent of the country’s GDP. The findings show that government size reduces the size of the shadow economy in the short run but increases it in the long run. The study also finds that interest rate, which is the opportunity cost of holding cash, and …

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 …

Impact Of Exchange Rate On Trade Flow In Nigeria, Victor U. Ijirshar, Isa J. Okpe, Jerome T. Andohol

CBN Journal of Applied Statistics (JAS)

This study examines the impact of exchange rate on trade flow in Nigeria from 1986 to 2021. The study utilises linear and nonlinear autoregressive distributed lag (ARDL and NARDL) models to test the J-Curve hypothesis and the Marshall-Lerner condition in Nigeria. The study found symmetric effects of exchange rate on trade balance, exports, and imports. The findings also show that real exchange rate depreciation has a strong negative influence on trade balance and exports in the short run but positive in the long run, exhibiting the shape typology of the J-curve. Furthermore, the study reveals evidence of the Marshall-Lerner condition …

Statistical Methods For Meta-Analysis In Large-Scale Genomic Experiments, Wimarsha Thathsarani Jayanetti

Mathematics & Statistics Theses & Dissertations

Recent developments in high throughput genomic assays have opened up the possibility of testing hundreds and thousands of genes simultaneously. With the availability of vast amounts of public databases, researchers tend to combine genomic analysis results from multiple studies in the form of a meta-analysis. Meta-analysis methods can be broadly classified into two main categories. The first approach is to combine the statistical significance (pvalues) of the genes from each individual study, and the second approach is to combine the statistical estimates (effect sizes) from the individual studies. In this dissertation, we will discuss how adherence to the standard null …

Efficient Hierarchical Space-Time Models For Large Areal Datasets With Application To Forest Inventory Mapping Using Remote Sensing Imagery, Md Kamrul Hasan Khan

Graduate Theses and Dissertations

The focus of this dissertation is development of a novel hierarchical framework, that can be used for predictive modeling of Forest Inventory and Analysis (FIA) data over large regions. This dissertation has two significant contributions. Based on a study region in north-central Wisconsin, we analyze satellite imagery, along with a sample of national forest inventory field plots, to monitor and predict changes in forest conditions over time. The auxiliary data from the satellite imagery of this region are relatively dense in space and time, and can be used to learn how forest conditions changed over that decade. However, these records …