Statistics and Probability Commons^™

Application Of Sentiment Analysis And Machine Learning Techniques To Predict Daily Cryptocurrency Price Returns, Edward Wu

CMC Senior Theses

This paper examines the effects of social media sentiment relating to Bitcoin on the daily price returns of Bitcoin and other popular cryptocurrencies by utilizing sentiment analysis and machine learning techniques to predict daily price returns. Many investors think that social media sentiment affects cryptocurrency prices. However, the results of this paper find that social media sentiment relating to Bitcoin does not add significant predictive value to forecasting daily price returns for each of the six cryptocurrencies used for analysis and that machine learning models that do not assume linearity between the current day price return and previous daily price …

Modeling Growth And Stress Factors For Converted Silvopasture Systems In The Missouri Ozarks, Bailee N. Suedmeyer

Graduate Theses/Dissertations

Silvopasture systems are becoming increasingly popular among sustainable agriculture ranchers, due to the increase in knowledge of benefits to the cattle and ability to grow cool season grasses beneath the canopy. This project focuses on the forest crop aspect of silvopasture systems from monitoring of the health of the trees over time to recommendations for thinning management to keep it functioning as viable silvopasture. The study site consists of five acres of upland hardwood forest area in Southern Missouri with 18 monumented fixed area plots. Arial and ground data was collected at each plot throughout the growing season, along with …

Meta-Analysis Of Mesenchymal Stem Cell Gene Expression Data From Obese And Non-Obese Patients, Dakota William Shields

Masters Theses

"The prevalence of gene expression microarray datasets in public repositories gives opportunity to analyze biologically interesting datasets without running the laboratory aspect in house. Such experimentation is expensive in terms of finances, time, and expertise, which often results in low numbers of replicates. Meta-analysis techniques attempt to overcome issues due to few biological or technical replicates by combining separate experiments together to increase statistical power. Proper statistical considerations help to offset issues like simultaneous testing of thousands of genes, unintended hybridization, and other noises.

Microarrays contain light intensities from tens of thousands of hybridized probes giving a measure of gene …

Thai Language Sentiment Analysis With A Hybrid Method On Wangchanberta-Cnn-Bilstm, Kasidhit Suraratchai

Chulalongkorn University Theses and Dissertations (Chula ETD)

Understanding emotions conveyed in text, especially in non-global languages such as Thai, sentiment analysis is particularly important in Thailand. However, this endeavor faces challenges due to variations in text length, which significantly impact sentiment analysis outcomes. Previous research has employed neural network and machine learning models in the process from research [2] and [7], yet each model specializes in different aspects, making comprehensive sentiment analysis coverage unattainable. Recent research [13], has delved into hybrid models like CNN-BiLSTM and BiLSTM-CNN. Although they demonstrate efficacy, their performance still varies across different datasets. For instance, CNN-BiLSTM excels with short sentences by considering surrounding …

การศึกษาประสิทธิกาพการกำหนดช่วงเวลาการซ่อมบำรุงแบบปรับเปลี่ยน จากข้อมูลที่มาจากการแจกแจงไวบูลล์, ณัฐพงศ์ อนันต์อาชญาสิทธิ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้มีวัตถุประสงค์ดังนี้(1)เพื่อเปรียบเทียบค่าใช้จ่ายในการซ่อมบำรุงแบบปรับเปลี่ยน (Adaptive Maintenance) จากการกำหนดช่วงการซ่อมบำรุงโดยใช้เปอร์เซ็นต์ไทล์ของอุปกรณ์ที่มีการแจกแจงแบบไวบูลล์ และ(2)เพื่อเปรียบเทียบประสิทธิภาพในการประมาณค่าพารามิเตอร์จากข้อมูลที่ถูกตัดทิ้ง โดยใช้ค่า MSE ในการเปรียบเทียบประสิทธิภาพ โดยการศึกษามีขั้นตอนดังนี้ 1. ใช้ข้อมูลจำลองที่มาจากการแจกแจงไวบูลล์ (Weibull distributions) ซึ่งมีอัตราการเสีย (Failure Rate) ที่แตกต่างกัน, 2. ช่วงเวลาซ่อมบำรุงคำนวณจากค่าเปอร์เซ็นไทล์ ตั้งแต่เปอร์เซ็นไทล์ที่ 5, 10, ... ถึง 95 3. เปรียบเทียบค่าใช้จ่าย ด้วยอัตราส่วนค่าใช้จ่ายระหว่างการซ่อมบำรุงเชิงแก้ไข (corrective maintenance)และการซ่อมบำรุงเชิงป้องกัน (maintenance preventive) 4.กำหนดเวลาในการสังเกตุโดยรวม 100 หน่วยเวลา ผลการวิจัยประกอบด้วย(1)เมื่อพิจารณาค่าใช้จ่ายซ่อมบำรุงจากข้อมูลแสดงอัตราการเสียที่ลดลง และ คงที่ตามเวลา การใช้เปอร์เซ็นต์ไทล์ที่ 95 จะมีค่าใช้จ่ายซ่อมบำรุงที่น้อยที่สุดเมื่อเปรียบเทียบกับการใช้เปอร์เซ็นต์ไทล์ค่าอื่นในสัดส่วนค่าใช้จ่ายเดียวกัน แต่ในกรณีที่ข้อมูลแสดงอัตราการเสียเพิ่มขึ้นตามเวลา ค่าใช้จ่ายซ่อมบำรุงที่น้อยที่สุด จะขึ้นอยู่กับสัดส่วนค่าใช้จ่ายและการเลือกใช้เปอร์เซ็นต์ไทล์ และ(2)ประสิทธิภาพในการประมาณค่าพารามิเตอร์ ในทุกกรณีของข้อมูลแสดงอัตราการเสียลดลง คงที่ และเพิ่มขึ้นตามเวลา การใช้เปอร์เซ็นต์ไทล์ที่มีค่าสูง จะทำให้การประมาณมีประสิทธิภาพสูงสุด (ค่า MSE มีค่าน้อยที่สุด)

การเปรียบเทียบประสิทธิภาพในการปรับปรุงคุณภาพของภาพด้วยวิธีระบบอนุมานฟัซซีและตัวกรองพื้นฐานในการจำแนกเนื้องอกสมองจากภาพเอ็มอาร์ไอ, กัทลี แก้วน่วม

Chulalongkorn University Theses and Dissertations (Chula ETD)

เนื้องอกสมองสามารถพบได้ทุกเพศทุกวัยและอาจส่งผลร้ายแรงถึงขั้นเสียชีวิตได้ โดยเนื้องอกสมองสามารถแบ่งได้ 3 ชนิดหลักๆ คือ เนื้องอกไกลโอมา เนื้องอกเยื่อหุ้มสมอง และเนื้องอกต่อมใต้สมอง การวินิจฉัยเนื้องอกสมองที่มีประสิทธิภาพและแพร่หลายในปัจจุบันคือการวินิจฉัยจากภาพเอ็มอาร์ไอแต่ยังมีข้อเสียคือมีสัญญาณรบกวนภายในภาพทำให้ประสิทธิภาพในงานวินิจฉัยลดลง งานวิจัยนี้เปรียบเทียบวิธีการปรับปรุงคุณภาพภาพเอ็มอาร์ไอเนื้องอกสมองที่จะช่วยเพิ่มประสิทธิภาพในการจำแนกชนิดเนื้องอกสมองเมื่อผ่านเรียนรู้ผ่านตัวแบบ Xception โดยวิธีที่นำมาเปรียบเทียบมี 4 วิธีได้แก่ ระบบอนุมานฟัซซี ตัวกรองมัธยฐาน ตัวกรองมัธยฐานแบบปรับตัวได้ และตัวกรองวีเนอร์ ชุดข้อมูลที่ใช้ในงานวิจัยนี้เป็นภาพเอ็มอาร์ไอจาก Nanfang Hospital and General Hospital, Tianjin Medical University ระหว่างปี 2005-2010 จำนวน 3,064 ภาพ พบว่าภาพเอ็มอาร์ไอเนื้องอกสมองที่ผ่านการปรับปรุงด้วยระบบอนุมานฟัซซีใช้ Mask ขนาด 3x3 มีประสิทธิภาพสูงสุด เนื่องจากให้ค่าความถูกต้อง เท่ากับ 0.9612 ค่าความครบถ้วนเท่ากับ 0.9627 และค่า F1-Score เท่ากับ 0.9602 ซึ่งสูงกว่าภาพเอ็มอาร์ไอเนื้องอกสมองที่ผ่านการปรับปรุงด้วยวิธีการอื่นๆรวมทั้งสูงกว่าภาพเอ็มอาร์เนื้องอกสมองที่ไม่ผ่านการปรับปรุงคุณภาพ ดังนั้นการปรับปรุงคุณภาพของภาพเอ็มอาร์ไอก่อนนำภาพไปวินิจฉัยจะช่วยเพิ่มประสิทธิภาพในการจำแนกเนื้องอกสมองให้มีความถูกต้องเพิ่มขึ้น

Evaluation Of Edison's Data Science Competency Framework Through A Comparative Literature Analysis, Karl R. B. Schmitt, Linda Clark, Katherine M. Kinnaird, Ruth E. H. Wertz, Björn Sandstede

Statistical and Data Sciences: Faculty Publications

During the emergence of Data Science as a distinct discipline, discussions of what exactly constitutes Data Science have been a source of contention, with no clear resolution. These disagreements have been exacerbated by the lack of a clear single disciplinary 'parent.' Many early efforts at defining curricula and courses exist, with the EDISON Project's Data Science Framework (EDISON-DSF) from the European Union being the most complete. The EDISON-DSF includes both a Data Science Body of Knowledge (DS-BoK) and Competency Framework (CF-DS). This paper takes a critical look at how EDISON's CF-DS compares to recent work and other published curricular or …

Utilizing Markov Chains To Estimate Allele Progression Through Generations, Ronit Gandhi

Honors Program: Senior Projects (Public)

All populations display patterns in allele frequencies over time. Some alleles cease to exist, while some grow to become the norm. These frequencies can shift or stay constant based on the conditions the population lives in. If in Hardy-Weinberg equilibrium, the allele frequencies stay constant. Most populations, however, have bias from environmental factors, sexual preferences, other organisms, etc. We propose a stochastic Markov chain model to study allele progression across generations. In such a model, the allele frequencies in the next generation depend only on the frequencies in the current one.

We use this model to track a recessive allele …

Identifying Potential Consequences Of Use Of A Measure Through Stakeholder Interviews, Melissa G. Kuhn, Joanna K. Garner, Shanan L. Chappell, Linda Bol

STEMPS Faculty Publications

The advent of Messick’s Contemporary Validity Theory (1994) led researchers in survey and measure design to develop and standardize qualitative and quantitative methods of collecting evidence for the validity of psychological instruments. However, one aspect of this theory, consequential validity, has few agreed-upon evidence-gathering methods (Furr & Bachrach, 2014). This study added a blueprint-driven interview method of collecting forecasted consequences of use of an instrument into the overall process of developing a measure of outreach impact on undergraduate engineering students (Authors, in press). The method, which included a purposeful sampling of end-users, is illustrated with thematically presented results. Potential implications …

Statistical Intervals For Neural Network And Its Relationship With Generalized Linear Model, Sheng Yuan

Theses and Dissertations--Statistics

Neural networks have experienced widespread adoption and have become integral in cutting-edge domains like computer vision, natural language processing, and various contemporary fields. However, addressing the statistical aspects of neural networks has been a persistent challenge, with limited satisfactory results. In my research, I focused on exploring statistical intervals applied to neural networks, specifically confidence intervals and tolerance intervals. I employed variance estimation methods, such as direct estimation and resampling, to assess neural networks and their performance under outlier scenarios. Remarkably, when outliers were present, the resampling method with infinitesimal jackknife estimation yielded confidence intervals that closely aligned with nominal …

Probabilistic Methods For Inferring The Order Of Pathway Alterations During Carcinogenesis And Cancer Subtype Classification, Menghan Wang

Theses and Dissertations--Statistics

Carcinogenesis is a complex process involving somatic mutations in a number of key biological pathways. Studying cancer evolution is an important task which contributes to better understanding of cancer biology and facilitates identification of new therapeutic targets. We focus on two important questions in cancer evolution. The first question is to delineating the temporal order of pathway mutations during tumorigenesis. And the other question is to cluster patients into biologically meaningful cancer subtypes. We present new statistical methods to 1)leverage functional annotations of mutations to enhance estimation of the order of pathway mutations during carcinogenesis, 2) incorporate intra-tumoral heterogeneity information …

Tolerance Intervals For Various Regression Models, Xitong Zhou

Theses and Dissertations--Statistics

Among statistical intervals, confidence intervals and prediction intervals are well-known and commonly used. In many applications, the problem becomes finding an interval that covers at least a certain proportion $P$ of the population for a characteristic of interest with a specified confidence level $(1-\alpha)$. And such interval is named a $P$-content, $(1-\alpha)$-confidence Tolerance Interval (TI). The topic of the dissertation is the utility of tolerance intervals for various regression models. We begin with a discussion of tolerance intervals for linear and nonlinear regression models. We then propose a bootstrap method of constructing TIs for Tobit regression to deal with censored …

High Dimensional Data Analysis: Variable Screening And Inference, Lei Fang

Theses and Dissertations--Statistics

This dissertation focuses on the problem of high dimensional data analysis, which arises in many fields including genomics, finance, and social sciences. In such settings, the number of features or variables is much larger than the number of observations, posing significant challenges to traditional statistical methods.

To address these challenges, this dissertation proposes novel methods for variable screening and inference. The first part of the dissertation focuses on variable screening, which aims to identify a subset of important variables that are strongly associated with the response variable. Specifically, we propose a robust nonparametric screening method to effectively select the predictors …

Novel Modelling And Inference Considerations Involving The Exponentially-Modified Gaussian Distribution, Yanxi Li

Theses and Dissertations--Statistics

The exponentially-modified Gaussian (EMG) distribution is well-suited for analyzing data with positive skewness due to its characteristic positive skew from the exponential component. Despite its popularity in various fields, the EMG distribution has only been analyzed for univariate data without any regression settings. To address this limitation, we developed a generalized EMG regression model with covariates by assigning parametric functional forms to some or all of the parameters in the EMG distribution that vary with values of the covariates. To further perform data-clustering on observation points, we propose a competing regression model where the error structure is assumed to be …

Finite Mixtures Of Mean-Parameterized Conway-Maxwell-Poisson Models, Dongying Zhan

Theses and Dissertations--Statistics

For modeling count data, the Conway-Maxwell-Poisson (CMP) distribution is a popular generalization of the Poisson distribution due to its ability to characterize data over- or under-dispersion. While the classic parameterization of the CMP has been well-studied, its main drawback is that it is does not directly model the mean of the counts. This is mitigated by using a mean-parameterized version of the CMP distribution. In this work, we are concerned with the setting where count data may be comprised of subpopulations, each possibly having varying degrees of data dispersion. Thus, we propose a finite mixture of mean-parameterized CMP distributions. An …

Methodologies And Computational Tools For Zero-Inflated Discrete Weibull Models, Peng Yeh

Theses and Dissertations--Statistics

Count data with excess zeros is common in many fields, such as ecology, healthcare, and insurance. Excess zeros data are often causing the inaccurate fit from the count models. While zero-inflated models have been developing for over two decades, one should also consider a more flexible model that can handle the excess zeros and further over- or under-dispersion. In this talk, we discuss zero-inflated discrete Weibull model and some novel computational contributions. The flexibility and competitiveness of the ZIDW model are illustrated by simulation studies and a real data analysis. We also investigate the performance of the proposed model through …

Striving For Appropriate Antibiotic Use: A Biomarker Initiative, And Outcomes Associated With Azithromycin Exposure, Amanda Gusovsky

Theses and Dissertations--Pharmacy

The introduction of antibiotics into clinical practice is considered the greatest medical breakthrough of the 20^thcentury. However, the use of antibiotics can contribute to the development of resistance. In the United States (U.S.), approximately 2.8 million people are infected with antibiotic-resistant bacteria each year, and more than 35,000 people die as a result. Moreover, some antibiotics are known to cause cardiac side effects including QT prolongation, hypotension, and ventricular arrythmias. The U.S. Centers for Disease Control and Prevention (CDC) defines appropriate antibiotic use as the effort to use “the right antibiotic, at the right dose, for the right …

Clustering Hospital Performance Using Group-Based Multi-Trajectory Modeling With Singular Bayesian Information Criterion, Gaixin Du

Theses and Dissertations--Epidemiology and Biostatistics

Hospital performance is complex and patient-experience oriented. Currently, the Centers for Medicare and Medicaid Services (CMS) evaluate hospitals yearly with a single score of one to five ("Star Rating") using composite measures from five domains. However, a single composite score cannot fully describe it, and alternative measures should be considered. Healthcare quality improvement needs long-term data to validate effectiveness. Group-based multi-trajectory modeling (GBMTM) estimates probabilities of latent group membership based on longitudinal profiles from multiple outcomes. We use GBMTM to identify groups of hospitals with similar performance in SAS PROC TRAJ.

We downloaded Medicare-eligible hospitals (N=5,111) that provided patient care …

Potential Alzheimer's Disease Plasma Biomarkers, Taylor Estepp

Theses and Dissertations--Epidemiology and Biostatistics

In this series of studies, we examined the potential of a variety of blood-based plasma biomarkers for the identification of Alzheimer's disease (AD) progression and cognitive decline. With the end goal of studying these biomarkers via mixture modeling, we began with a literature review of the methodology. An examination of the biomarkers with demographics and other health factors found evidence of minimal risk of confounding along the causal pathway from biomarkers to cognitive performance. Further study examined the usefulness of linear combinations of biomarkers, achieved via partial least squares (PLS) analysis, as predictors of various cognitive assessment scores and clinical …

Economics Of Maple Syrup Production In Kentucky, Bobby Thapa

Theses and Dissertations--Forestry and Natural Resources

Maple syrup production is traditionally associated with New England regions in the United States, but there is growing interest in expanding it to other regions with suitable environmental conditions, including Kentucky. This study presents an in-depth analysis of the potential production and economic impacts of maple syrup in Kentucky using a multi-method approach. First, the study applies a stochastic production model to assess the effects of climatic and tree variables on maple syrup yield. The results reveal that several variables, including the number of maple trees, taps, temperatures, tapping season length, and time, significantly affect maple syrup yield. Second, input-output …