Statistics and Probability Commons^™

Bayesian Structural Time Series Methods For Modeling Cattle Body Temperature In Heat-Stressed Animals, Lacey Quandt

Murray State Theses and Dissertations

Climate change has had devastating effects globally, most commonly talked about during natural disasters and rising temperatures. Notably, the climate concern is turning towards agriculture and livestock. With rising temperatures, the prolonged amount of heat stress put on animals, specifically cattle, is becoming more apparent. Heat stress has been linked to a reduction in cattle growing and fattening, feed intake, productivity, reproduction, and fertility; increased heart rates and respiration; changes in behavior; and mortality in severe cases. There are abatement strategies put in place to lower heat stress in cattle, such as improvements in shading and cooling, nutritional management, and …

Automated Machine Learning: Intellient Binning Data Preparation And Regularized Regression Classfier, Jianbin Zhu

Electronic Theses and Dissertations, 2020-2023

Automated machine learning (AutoML) has become a new trend which is the process of automating the complete pipeline from the raw dataset to the development of machine learning model. It not only can relief data scientists' works but also allows non-experts to finish the jobs without solid knowledge and understanding of statistical inference and machine learning. One limitation of AutoML framework is the data quality differs significantly batch by batch. Consequently, fitted model quality for some batches of data can be very poor due to distribution shift for some numerical predictors. In this dissertation, we develop an intelligent binning to …

Applications For Functional Data Analysis, Kacy D. Kane

Graduate Research Theses & Dissertations

Functional Data Analysis is often used in the study of data that exists over a continuum, such as time. There are two datasets that will be considered here. For the first study we have a dataset on the efficacy of a lobectomy in reduction or elimination of epileptic seizures in patients. After an initial analysis of the dataset from a multinomial model perspective, we found that there were outliers in our dataset. From there, we considered a Multinomial Mixture Model to aid in the detection of outliers. In our second dataset we are considering a social robotics dataset where the …

Macroeconomic Factors Influencing Foreign Direct Investment In Some Selected Countries In Africa, Richard Essel Mensah

Graduate Research Theses & Dissertations

This paper investigates the possible factors that influence foreign direct investment inflow rate to Africa after controlling for other macroeconomic factors. Using the heterogenous Toeplitz mixed method on a sample of 23 countries from 1998 – 2020, we find evidence of the statistical significance of a relationship between the amount of trade done in Africa and the FDI inflow rate in Africa. We also find a statistical relationship between the labor force participation rate and the FDI inflow rate to Africa. Although the Fixed effect and GLM method did not find the relationship between LFP rate and FDI inflow to …

Impacts Of Covid-19 On Industrial Growth In The United States, Emily G. Warthman, Charles J. Landis

Williams Honors College, Honors Research Projects

COVID-19 has caused massive ramifications on all parts of life in the world and industry growth/decline is not immune to it. This report will analyze nine different industries’ profit and revenue from quarterly data during the years 2009-2022. Forecast models will be generated using various methods and different techniques of validating to predict the values from Q2 2020- Q4 2022 based on historical data. After which, a comparison will be conducted between those predicted values to the actual average revenue and profit generated by order of greatest error percentage made. Thorough research will then be completed to determine if there …

Modeling The Bidirectional Relationship Between Shared-Patient Physician Networks And Patient Longitudinal Treatment Patterns: Application To Physician Risky-Prescribing, Xin Ran

Dartmouth College Ph.D Dissertations

Risky-prescribing is a pressing public health concern in the United States. Opioids, benzodiazepines, and non-benzodiazepine sedative-hypnotics (sedative-hypnotics) are three commonly-prescribed but potentially risky drug groups, prescribed alone or in combination. Physician shared-patient networks provide a unique perspective in studying physician network characteristics and structures, as well as their association with the delivery of health care. Understanding how physician shared-patient networks are related to their prescribing may inform network-based interventions targeting risky-prescribing, which is yet to be fully studied.

We investigated patient receipt of risky prescriptions and physician risky-prescribing intensity through the scope of shared-patient networks. We used retrospective Medicare insurance …

The Influence Of Instrumental Sources Of Variance On Mass Spectral Comparison Algorithms, Isabel Cristina Galvez Valencia

Graduate Theses, Dissertations, and Problem Reports (ETD)

Current search algorithms for the identification of substances based only on their electron ionization mass spectra provide the correct compound as their top result approximately 80% of the time. One contributing factor to the ~20% deviation in the first-hit recognition rate is that traditional algorithms work by comparing the unknown spectrum to an ‘ideal’ or consensus spectrum of each reference compound. The inclusion of replicate reference spectra in a database has been shown to improve the probability of ranking the correct identity in the number one position, but the variance in ion abundances caused by different conditions or different instruments …

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. …

The Influence Of Urban Forms And Street Infrastructure On Pedestrian-Motorist Collisions, Taylor J. Foreman

College of Graduate Studies: Theses & Dissertations

Unwalkable cities are afflicted by serious issues such as increasing rates of pedestrian traffic accidents, public health concerns, and the denied right to have an accessible city. This study examines how different types of urban forms and street infrastructure contribute to the prevalence of traffic accidents in two major metropolitan cities in the United States: Atlanta, Georgia, and Boston, Massachusetts. This study utilizes geospatial analysis through the Average Nearest Neighbor and Optimized Hot Spot Analysis tools to determine the spatial distribution of traffic accidents throughout both cities. Additionally, statistical tests were conducted to explore the relationships between the number of …

Variability In Causal Effects On A Binary Outcome And Noncompliance In A Multisite Randomized Trial, Xinxin Sun

Theses and Dissertations

Noncompliance to treatment assignment is widespread in randomized trials and presents challenges in causal inference. In the presence of noncompliance, the most commonly estimated effect of treatment assignment, also known as intent-to-treat (ITT) effect, is biased. Of interest in this setting is the complier average causal effect (CACE), the ITT effect among compliers. Further complication arises when the outcome variable is partially observed.

My research focuses on estimating the distribution of a site-specific CACE in a multisite randomized controlled trial (MRCT) by maximum likelihood (ML). Assuming compliance missing at random (MAR). We express the likelihood as an integral with respect …

Early Termination In Phase Ii Clinical Trials: Admissible Designs Using Decreasingly Informative Priors, Chen Wang

Theses and Dissertations

In Phase II clinical trials, Thall and Simon’s Bayesian posterior probability design is commonly implemented to allow for an early termination to determine whether a new treatment warrants further investigation in a larger-scale Phase III trial; this in turn requires a pre-selected prior distribution based on known clinical opinion or historical information. Moreover, this Bayesian approach can result in an issue of inflating type I error rate by monitoring interim data to inform early termination decisions. Alternatively, a Bayesian approach with the decreasingly informative prior (DIP), which is an informative yet skeptical prior, can be implemented to overcome the contentious …

Model-Based Imputation Of Below Detection Limit Missing Data And Group Selection In Bayesian Group Index Regression, Matthew Carli

Theses and Dissertations

Investigations into the association between chemical exposure and health outcomes are increasingly focused on the role of chemical mixtures, as opposed to individual chemicals. The analysis of chemical mixture data required the development of novel statistical methods, one of these being Bayesian group index regression. A statistical challenge common to all chemical mixture analyses is the ubiquitous presence of below detection limit (BDL) data. We propose an extension of Bayesian group index regression that treats both regression effects and missing BDL observations as parameters in a model estimated through a Markov Chain Monte Carlo algorithm that we refer to as …

Essays On Conditional Heteroscedastic Time Series Models With Asymmetry, Long Memory, And Structural Changes, K C M R Anjana Bandara Yatawara

Doctoral Dissertations

"The volatility of asset returns is usually time-varying, necessitating the introduction of models with a conditional heteroskedastic variance structure. In this dissertation, several existing formulations, motivated by the Generalized Autoregressive Conditional Heteroskedastic (GARCH) type models, are further generalized to accommodate more dynamic features of asset returns such as asymmetry, long memory, and structural breaks. First, we introduce a hybrid structure that combines short-memory asymmetric Glosten, Jagannathan, and Runkle (GJR) formulation and the long-memory fractionally integrated GARCH (FIGARCH) process for modeling financial volatility. This formulation not only can model volatility clusters and capture asymmetry but also considers the characteristic of long …

การเปรียบเทียบวิธีการใส่ค่าสูญหาย ในการวิเคราะห์การถดถอยโลจิสติก เมื่อตัวแปรตามมีการสูญหายแบบนอนอิกนอร์เรเบิล, อภิชาติ ฉัตรเรืองเลิศ

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้มีวัตถุประสงค์เพื่อเปรียบเทียบวิธีการใส่ค่าสูญหาย ในการวิเคราะห์การถดถอยโลจิสติก เมื่อตัวแปรตามมีการสูญหายแบบนอนอิกนอร์เรเบิล วิธีการที่ใช้ศึกษา คือ วิธี Complete Case Analysis (CC) วิธี Mode Imputation (MODE) วิธี Expectation Maximization Algorithm (EM) วิธี Multiple Imputation (MI) วิธี Hard Cutoff Augmentation (HARDCUT) วิธี Parceling Augmentation (PARCELING) และวิธี Fuzzy Augmentation (FUZZY) งานวิจัยนี้ใช้การจำลองข้อมูลในการศึกษาตามขนาดของตัวอย่าง ร้อยละของการสูญหายของข้อมูล และระดับของการสูญหายแบบนอนอิกนอร์เรเบิล การจำลองข้อมูลในแต่ละสถานการณ์จะกระทำ 5,000 รอบ โดยมีเกณฑ์ที่ใช้เปรียบเทียบประสิทธิภาพของวิธีการใส่ค่าสูญหาย ได้แก่ ค่าเฉลี่ยของค่าเฉลี่ยความคลาดเคลื่อนกำลังสอง (Average Mean Squared Error: AMSE) ของค่าประมาณความน่าจะเป็นของการเกิดเหตุการณ์ที่สนใจ (P(Y = 1)) และค่าประสิทธิภาพสัมพัทธ์ (Relative Efficiency: RE) จากผลการทดลองสรุปได้ว่า ค่า AMSE จะลดลงเมื่อขนาดของตัวอย่างใหญ่ขึ้น และจะมีค่ามากขึ้นเมื่อร้อยละของการสูญหายของข้อมูลเพิ่มขึ้น เมื่อพิจารณาผลของระดับของการสูญหายแบบนอนอิกนอร์เรเบิลต่อค่า AMSE พบว่ามีเพียง AMSE ของวิธี MODE เท่านั้นที่มีแนวโน้มเพิ่มขึ้น เมื่อระดับของการสูญหายแบบนอนอิกนอร์เรเบิลเพิ่มขึ้น และเมื่อพิจารณาค่า RE โดยเปรียบเทียบ AMSE ของวิธี CC กับวิธีการใส่ค่าสูญหายวิธีอื่น พบว่า วิธี EM และวิธี FUZZY ให้ค่า AMSE เท่ากับ AMSE ของวิธี CC ในขณะที่ AMSE ของวิธีอื่น ๆ มีค่าน้อยกว่า AMSE ของวิธี CC

Integrative Post-Gwas Analyses Of Psychiatric Disorders: Identifying Putative Risk Genes And Gene Sets Using Transcriptome, Proteome And Methylome Information, Huseyin Gedik

Theses and Dissertations

Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often they do not implicate specific protein coding genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS), which integrate information from GWAS and x-level (mRNA, protein or DNA methylation levels) coming from largest xQTL studies, can link GWAS signals to effects on specific genes. For gene level analyses, researchers use mendelian randomization (MR) methods to fine-map the association between x-levels and trait. However, none of the previous studies ever jointly analyzed XWAS of multiple …

Reassessing Replication: Addressing The Replication Crisis From A Statistical Perspective, Alicia Richards Phd

Theses and Dissertations

In 2015, Open Science Framework directly replicated 100 psychology studies and found astonishingly low replication rates. Since, researchers have suggested factors that may have influenced the low rates, including the metrics used to assess replications. The definitions used to decide whether a replication study was successful all suffer from flaws. Therefore, we propose a new metric for assessing replication that can estimate the likelihood a study successfully replicated rather than forcing a binary choice and accounts for study design limitations.

Using equivalence study techniques, we first propose a new metric to assess replication, defining a successful replication as one where …

Dynamic Overnight Effect On Next Day Stock Market Forecasting, Thomas J. Lee

Graduate Research Theses & Dissertations

Using a cross section of stocks that have high frequency trading data from 2007 to 2018, we document whether various intraday momentum patterns found in the financial literature over the years continue to hold over time. The first half hour return on the market is often seen as having predictive power over the last half hour of trading, or overnight returns are thought to reverse in the next day's first half hour of trading. We find that while there is some evidence for these patterns, especially in the earlier years, these patterns tend to weaken over time as investors take …

The Impact Of Faculty Composition On Cost Per Student: A Mixed Model Approach, Arun Sleeba

Graduate Research Theses & Dissertations

This thesis aims to explore whether research universities in the United States, specifically those classified as Carnegie I or II institutions, utilize part time contingent faculty(rPTF) as a cost saving strategy. Additionally, it sought to determine if there was a differential impact on total costs when comparing public and private universities. Employing a linear mixed effects model with random intercept and slopes, this study analyzed the relationship between rPTF (ratio of part-time to total faculty) and total cost. This study did not provide substantial evidence to support the notion despite observing a negative correlation between rPTF and total cost. Regarding …

Enhanced Maximum Likelihood Models For Underreported Variables: Extending To Multiple Claims Dimension, Shalaka Sudhanshu Sarpotdar

Graduate Research Theses & Dissertations

This thesis builds upon the foundations laid out in Xia et al. [2023], which explored the utilizationof Maximum Likelihood approach to model misrepresentation data in Generalized Linear Models (GLM) ratemaking models. We introduce the concept of “underreported variables”, a form of insurance misrepresentation where insured individuals provide inaccurate information about risk factors that influence insurance eligibility, premiums, and insured amounts. Unlike fraudulent misrepresentation, underreported variables arise from a lack of awareness regarding the insured’s mental and physical health conditions, rather than fraudulent intent. The study rigorously tests the proposed model using health insurance data and extends its applicability to other …

A State Space Modeling Approach To Eeg Artifact Removal, Patrick B. Rafael

Graduate Research Theses & Dissertations

In this work, a state space modeling approach is applied to an Electroencephalography(EEG) recording for the purpose of artifact removal, and is compared against Independent Components Analysis (ICA), the current gold standard. Issues of model identifiability are touched on, and Hamiltonian Monte Carlo (HMC) is used to estimate a linear non-Gaussian state space model. Results show that estimating such a model is a nontrivial matter, and the full utility of the state space approach remains to be demonstrated.