Statistics and Probability Commons^™

The Effect Of Time And Temperature On The Quality Of Latent Fingerprints On Incandescent Lightbulbs, Varying Donors Age And Sex, Kinaysha M. Collazo Maldonado

Master of Science in Forensic Science Directed Research Projects

Fingerprints are used as a means of identification, but there are no established methodologies to determine time since deposition of latent fingerprints by visual means alone. This research considered the influence of age and sex on the quality of recovered latent prints from lit and unlit lightbulbs from 1 to 10 days, using accumulated degree hours (ADH) to account for both heat and time simultaneously. Two male and two female donors (one of each aged40 years) were used. A thermal imaging camera was used to monitor the lightbulbs top and middle regions, which were significantly different (p≤0.05) for the experimental …

The Role Of Topography, Soil, And Remotely Sensed Vegetation Condition Towards Predicting Crop Yield, Trenton E. Franz, Sayli Pokal, Justin P. Gibson, Yuzhen Zhou, Hamed Gholizadeh, Fatima Amor Tenorio, Daran Rudnick, Derek M. Heeren, Matthew F. Mccabe, Matteo Ziliani, Zhenong Jin, Kaiyu Guan, Ming Pan, John Gates, Brian Wardlow

School of Natural Resources: Faculty Publications

Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what …

Distribution Of Human Exposure To Ozone During Commuting Hours In Connecticut Using The Cellular Device Network, Owais Gilani, Simon Urbanek, Michael J. Kane

Faculty Journal Articles

Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school, or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of …

Tropical Cyclone Hazards In Relation To Propagation Speed, Jiehao Huang

Dissertations and Theses

As the population and infrastructure along the US East Coast increase, it becomes increasingly important to study the characteristics of tropical cyclones that can impact the coast. A recent study shows that the propagation speed of tropical cyclones has slowed over the past 60 years, which can lead to greater accumulation of precipitation and greater storm surge impacts. The study presented herein is meant to examine and analyze the relationships that exist between the propagation speed of tropical cyclones, their surface wind strength, displacement angles, and cyclone averaged winds. This analysis is focused on tropical cyclones spanning from 1950-2015 in …

V-Slam And Sensor Fusion For Ground Robots, Ejup Hoxha

Dissertations and Theses

In underground, underwater and indoor environments, a robot has to rely solely on its on-board sensors to sense and understand its surroundings. This is the main reason why SLAM gained the popularity it has today. In recent years, we have seen excellent improvement on accuracy of localization using cameras and combinations of different sensors, especially camera-IMU (VIO) fusion. Incorporating more sensors leads to improvement of accuracy,but also robustness of SLAM. However, while testing SLAM in our ground robots, we have seen a decrease in performance quality when using the same algorithms on flying vehicles.We have an additional sensor for ground …

K-Means Stock Clustering Analysis Based On Historical Price Movements And Financial Ratios, Shu Bin

CMC Senior Theses

The 2015 article Creating Diversified Portfolios Using Cluster Analysis proposes an algorithm that uses the Sharpe ratio and results from K-means clustering conducted on companies' historical financial ratios to generate stock market portfolios. This project seeks to evaluate the performance of the portfolio-building algorithm during the beginning period of the COVID-19 recession. S&P 500 companies' historical stock price movement and their historical return on assets and asset turnover ratios are used as dissimilarity metrics for K-means clustering. After clustering, stock with the highest Sharpe ratio from each cluster is picked to become a part of the portfolio. The economic and …

How Machine Learning And Probability Concepts Can Improve Nba Player Evaluation, Harrison Miller

CMC Senior Theses

In this paper I will be breaking down a scholarly article, written by Sameer K. Deshpande and Shane T. Jensen, that proposed a new method to evaluate NBA players. The NBA is the highest level professional basketball league in America and stands for the National Basketball Association. They proposed to build a model that would result in how NBA players impact their teams chances of winning a game, using machine learning and probability concepts. I preface that by diving into these concepts and their mathematical backgrounds. These concepts include building a linear model using ordinary least squares method, the bias …

Parameter Estimation Of A Seasonal Poisson Inar(1) Model With Different Monthly Means, Turaj Vazifedan, Homa Jalaeian Taghadomi, Xixi Wang, Mujde Erten-Unal

Civil & Environmental Engineering Faculty Publications

Analysing seasonality in count time series is an essential application of statistics to predict phenomena in different fields like economics, agriculture, healthcare, environment, and climatic change. However, the information in the existing literature is scarce regarding the performances of relevant statistical models. This study provides the Yule-Walker (Y-W), Conditional Least Squares (CLS), and Maximum Likelihood Estimation (MLE) for First-order Non-negative Integer-valued Autoregressive, INAR(1), process with Poisson innovations with different monthly means. The performance of Y-W, CLS, and MLE are assessed by the Monte Carlo simulation method. The performance of this model is compared with another seasonal INAR(1) model by reproducing …

Inventory Models For Perishable Items Under Markdown Policy, Nurzahara Atika Kamaruzaman

Student Works (2020-2029)

As expected, the demand for a fresh product depends on how fresh it is, therefore, it is important to take expiration date into consideration. Based on marketing and economic theory, several factors such as price, inventory level and advertisement play a crucial role in influencing the demand. Hence, we study the effect of these factors in influencing the demand in the inventory model. Since the demand for perishable product declines over time, markdown policy is offered to increase the demand and profit while reducing the inventory. Salvage value is incorporated to the deteriorating units. In this research, we extend previous …

Mass Grave Localization Prediction With Geographical Information Systems In Guatemala And Future Impacts, Perla Santillan

Master of Science in Forensic Science Directed Research Projects

Conducting physical searches for mass grave locations based on anecdotal evidence is a time consuming and resource intensive endeavor in circumstances that often pose a threat to personal safety. The development of tools and procedures to speed such searches can greatly reduce the risk involved, increase the number of individuals whose remains are recovered and identified; and, more importantly, reunite these remains with their loved ones to provide them with a proper burial. Geographic information systems (GIS) software, which can analyze and manipulate the spatial characteristics of known mass grave data, represents a powerful tool that can be used to …

Bayesian Approach To Finding The Most Likely Circuit Structure, Shannon Harms

Graduate Research Theses & Dissertations

Systems, and their reliabilities, depend on the reliabilities of the components that theyare composed of, and in this paper we want to nd the system structure that is the most likely given observed data. Bayesian methods were utilized in order to discover the posterior means, or observed reliabilities, of both the components and the systems. Assuming the serial and parallel system structures have independent components, we calculated system reliabilities based on observed component reliabilities by using the multiplication and addi- tion probability rules. We are then able to expand upon the numerical comparison method through a maximum likelihood analysis that …

Construction Of Confidence Intervals For Parameter Estimates Of T-Distribution, Margaret Remus

Graduate Research Theses & Dissertations

Constructing confidence intervals is a standard statistical practice to estimate parameters of a distribution. Estimation of the parameters of a t-distribution can be computationally intensive. As a result, there are not clear defined formulas for the construction of confidence intervals for those estimates. Using the Normal confidence interval equation does not offer an accurate interval. Therefore, this paper will outline potential alternative methods for building confidence intervals to estimate the mean of a t-distribution. This paper will offer several methods and simulations to determine which method is best for constructing confidence intervals for varying sample sizes and degrees of freedom. …

Polarization In Social Networks Under A Proposed Model, Ashley L. Seyfried

Graduate Research Theses & Dissertations

The mathematical study of opinion dynamics started in 1956, with French. Since the explosion of social media, this study has increased in popularity. In this paper, we propose a new model to apply to a social network graph. With this model, the opinions of actors are in the range [0,1]. We use the Normal probability distribution to model these opinions in conjunction with a group of parameters. We allow for the uncertainty of members in the network. We then ran simulations to find the values of these parameters that do not lead to a network becoming polarized. Different states of …

A Mathematical Model For Malaria With Age-Heterogeneous Biting Rate, Sho Kawakami

All Graduate Theses, Dissertations, and Other Capstone Projects

We propose a mathematical model for malaria with age-heterogeneous biting rate from mosquitos. The existence of the model, the local behavior of the disease free equilibrium are explored. Furthermore the model is extended to an optimal control problem and the corresponding adjoint equations and optimality conditions are derived. Age dependent parameter values are estimated and numerical simulations are carried out for the model. The new model better accounts for difference in biting rates of mosquitos to different age groups, and improvements in stability to the explicit algorithm. The optimal control is also shown to depend on the age distribution of …

Cosmic: Us-Based Conversion Master's Degree In Computing, Gary S. Krenz, Thomas Kaczmarek

Mathematical and Statistical Science Faculty Research and Publications

COSMIC is an NSF S-STEM graduate curriculum initiative/conversion program that strives to provide an accelerated pathway to a Master of Science (MS) degree for individuals who do not have an undergraduate degree in computing, but who wish to cross over into the computing field. The structure of our conversion program, the context that motivated it, and insights from conversion students' instructors are presented. Program successes with students from under-represented populations and the limitations that are also experienced are discussed. Our conversion program is based on a highly focused summer bridge course, combined with a customized curriculum pathway that enables people …

Cubic Rank Transmuted Modified Burr Iii Distribution: Development, Properties, Characterizations And Applications, Fiaz Ahmad Bhatti, Gholamhossein Hamedani, Seyed Morteza Najibi, Munir Ahmad

Mathematical and Statistical Science Faculty Research and Publications

We propose a lifetime distribution with flexible hazard rate called cubic rank transmuted modified Burr III (CRTMBIII) distribution. We develop the proposed distribution on the basis of the cubic ranking transmutation map. The density function of CRTMBIII is symmetrical, right-skewed, left-skewed, exponential, arc, J and bimodal shaped. The flexible hazard rate of the proposed model can accommodate almost all types of shapes such as unimodal, bimodal, arc, increasing, decreasing, decreasing-increasing-decreasing, inverted bathtub and modified bathtub. To show the importance of proposed model, we present mathematical properties such as moments, incomplete moments, inequality measures, residual life function and stress strength reliability …

Fusion-Net: Integration Of Dimension Reduction And Deep Learning Neural Network For Image Classification, Mohammad Masum, Philippe Laval

Published and Grey Literature from PhD Candidates

Building a deep network using original digital images requires learning many parameters which may reduce the accuracy rates. The images can be compressed by using dimension reduction methods and extracted reduced features can be feeding into a deep network for classification. Hence, in the training phase of the network, the number of parameters will be decreased. Principal Component Analysis is a well-known dimension reduction technique that leverage orthogonal linear transformation of the original data. In this paper, we propose a neural network-based framework, named Fusion-Net, which implements PCA on an image dataset (CIFAR-10) and then a neural network applies on …

Fuzzy Logistic Regression For Detecting Differential Dna Methylation Regions, Tarek M. Bubaker Bennaser

Doctoral Dissertations

“Epigenetics is the study of changes in gene activity or function that are not related to a change in the DNA sequence. DNA methylation is one of the main types of epigenetic modifications, that occur when a methyl chemical group attaches to a cytosine on the DNA sequence. Although the sequence does not change, the addition of a methyl group can change the way genes are expressed and produce different phenotypes. DNA methylation is involved in many biological processes and has important implications in the fields of biomedicine and agriculture.

Statistical methods have been developed to compare DNA methylation at …

A Two-Stage Design For Comparing Binomial Treatments With A Standard, Cecelia K. Schmidt

UNF Graduate Theses and Dissertations

We propose a method for comparing success rates of several populations among each other and against a desired standard success rate. This design is appropriate for a situation in which all experimental treatments have only two outcomes that can be considered “success”and “failure” respectively. The goal is to identify which treatment has the highest rate of success that is also higher than the desired standard. The design combines elements of both hypothesis testing and statistical selection. At the first stage, if none of the samples have a number of successes above the appropriate standard for the design, the experiment is …

The Marshall-Olkin Exponentiated Generalized G Family Of Distributions: Properties, Applications, And Characterizations, Haitham M. Yousof, Mahdi Rasekhi, Morad Alizadeh, Gholamhossein Hamedani

Mathematical and Statistical Science Faculty Research and Publications

In this paper, we propose and study a new class of continuous distributions called the Marshall-Olkin exponentiated generalized G (MOEG-G) family which extends the Marshall-Olkin-G family introduced by Marshall and Olkin [A. W. Marshall, I. Olkin, Biometrika 84 (1997), 641-652]. Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, order statistics and probability weighted moments are derived. Some characteristics of the new family are presented. Maximum likelihood estimation for the model parameters under uncensored and censored data is addressed in Section 5 as well as a simulation study to assess the performance of …

การประยุกต์ใช้ตัวแบบซัพพอร์ตเวกเตอร์แมชชีนและตัวแบบโครงข่ายประสาทเทียมร่วมกับวิธีการทำให้เรียบแบบเอ็กซ์โปเนนเชียล, แซนนี่ ชัว

Chulalongkorn University Theses and Dissertations (Chula ETD)

การพยากรณ์อนุกรมเวลาเป็นการคาดคะเนผลลัพธ์จากข้อมูลในอตีต เพื่อเป็นแนวทางในการวางแผนการดำเนินงานด้านต่าง ๆ ในอนาคต งานวิจัยฉบับนี้ได้รวบรวมข้อมูลอนุกรมเวลาจากแหล่งข้อมูลต่าง ๆ ในประเทศไทยจำนวน 40 ชุด ทั้งปริมาณการผลิตสินค้า มูลค่าการจำหน่ายสินค้า ปริมาณเชื้อเพลิง ปริมาณน้ำในเขื่อน และจำนวนผู้ใช้บริการรถไฟฟ้า ซึ่งมีรูปแบบแนวโน้มและฤดูกาลของข้อมูลอนุกรมเวลาที่หลากหลาย สำหรับศึกษาการประยุกต์วิธีการทำให้เรียบแบบเอ็กซ์โปเนนเชียล (ES) ในการพยากรณ์ และเปรียบเทียบความแม่นยำของผลการพยากรณ์อนุกรมเวลาที่ได้จาก 3 ตัวแบบ คือ วิธีทำให้เรียบแบบเอ็กซ์โปเนนเชียล (ES), ตัวแบบผสมระหว่างตัวแบบ ES ร่วมกับตัวแบบโครงข่ายประสาทเทียม (ES+ANN) และตัวแบบผสมระหว่างตัวแบบ ES ร่วมกับตัวแบบซัพพอร์ทเวกเตอร์แมชชีน (ES+SVM) โดยมีเกณฑ์รากของค่าคลาดเคลื่อนกำลังสองเฉลี่ย (Root Mean Square Error : RMSE) เป็นเกณฑ์ในการเปรียบเทียบความแม่นยำของตัวแบบ ผลการศึกษาพบว่าตัวแบบผสมระหว่างตัวแบบ ES ร่วมกับตัวแบบโครงข่ายประสาทเทียม (ES+ANN) มีความแม่นยำในการพยากรณ์อนุกรมเวลามากที่สุดสำหรับข้อมูลทั้ง 40 ชุด

การศึกษาเปรียบเทียบการจำแนกประเภทข้อมูลคลื่นไฟฟ้าหัวใจด้วยการเรียนรู้เชิงลึกสำหรับข้อมูลคลื่นไฟฟ้าหัวใจที่มีสิ่งแปลกปน, ณัฏฐชัย บวรมงคลศักดิ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้มีวัตถุประสงค์เพื่อเปรียบเทียบตัวแบบในการจำแนกประเภทข้อมูลคลื่นไฟฟ้าหัวใจโดยใช้ตัวแบบการเรียนรู้เชิงลึก 3 ตัวแบบได้แก่ (1)โครงข่ายประสาทเทียมแบบเพอร์เซ็ปตรอนหลายชั้น (MLPs) (2)โครงข่ายคอนโวลูชันเต็มรูป (FCNs) และ (3)โครงข่ายแบบเรสซิดวลหรือเรสเนท (ResNet) ชุดข้อมูลที่ใช้ทดสอบประกอบด้วย 2 ส่วนได้แก่ ส่วนของข้อมูลจำลอง และส่วนของข้อมูลจริงใช้ข้อมูลจากฐานข้อมูล MIT-BIH Arrythmia ในแต่ละชุดข้อมูลจะทำการเพิ่มสิ่งแปลกปนในข้อมูล 4 แบบได้แก่ Wandering baseline, Muscle tremor, AC interference และ Motion artifacts และเปรียบเทียบประสิทธิภาพของแต่ละตัวแบบด้วยวิธีครอสวาลิเดชัน แบ่งข้อมูลเป็น 10 ส่วนแล้วพิจารณา ค่าความถูกต้อง ค่าความแม่นยำ และค่าความครบถ้วน สำหรับส่วนของข้อมูลจำลอง พบว่าเมื่อพิจารณาค่าความถูกต้องแล้ว ในภาพรวมตัวแบบ MLPs มีค่าเฉลี่ยที่ต่ำกว่า FCNs และ ResNet ค่อนข้างเยอะ ในขณะที่ตัวแบบ FCNs และ ResNet ได้ผลออกมาค่อนข้างดี และได้ผลลัพธ์ใกล้เคียงกันในข้อมูลแต่ละชุด ในชุดข้อมูลที่มีสิ่งแปลกปนประเภท Wandering baseline ค่าเฉลี่ยมีค่าลดลงสำหรับตัวแบบ MLPs ในชุดข้อมูลที่มีสิ่งแปลกปนประเภท Muscle tremor และ AC interference ไม่พบว่าค่าเฉลี่ยลดลงอย่างชัดเจนในทุกกรณี และในชุดข้อมูลที่มีสิ่งแปลกปนประเภท Motion artifacts พบว่าค่าเฉลี่ยลดลงเล็กน้อยเมื่อใช้ตัวแบบ MLPs สำหรับค่าความแม่นยำและค่าความครบถ้วนพบว่ามีทิศทางเดียวกับค่าความถูกต้อง สำหรับข้อมูลจริง ในภาพรวมมีความใกล้เคียงกับผลการศึกษาในส่วนของข้อมูลจำลอง แต่ในชุดข้อมูลที่มีสิ่งแปลกปน Wandering baseline พบว่าประสิทธิภาพลดลงในทุกตัวแบบ

การเปรียบเทียบวิธีบูตสแตรปในการประมาณช่วงความเชื่อมั่นของค่าสัมประสิทธิ์การถดถอยเชิงเส้นที่มีมิติสูง, ภูวกร นิธิศนทีกุล

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยฉบับนี้มีวัตถุประสงค์เพื่อศึกษาและเปรียบเทียบช่วงความเชื่อมั่นสำหรับค่าสัมประสิทธิ์การถดถอยโดยแนวทางบูตสแตรปที่แตกต่างกัน (1) วิธีสุ่มตัวแปรตามและตัวแปรอิสระ (2) วิธีสุ่มส่วนเหลือ และ (3) วิธีสุ่มค่าถ่วงน้ำหนัก ผู้วิจัยได้จำลองชุดข้อมูลขนาดมิติต่ำและมิติสูงขึ้น และ นำมาวิเคราะห์เปรียบเทียบด้วยวิธีบูตสแตปที่แตกต่างกัน 3 วิธี โดยการวัดค่าเฉลี่ยเปอร์เซ็นต์ช่วงความเชื่อมั่นที่ครอบคลุมค่าสัมประสิทธิ์การถดถอยค่าจริง ค่าเฉลี่ยความกว้าง ค่าเฉลี่ยอัตราผลบวกเทียม และค่าเฉลี่ยอัตราผลลบเทียม ระหว่าง 1,000 ข้อมูล การวิเคราะห์ของเราพบว่าบูตสแตรปที่ใช้สุ่มตัวแปรตามและตัวแปรอิสระดีที่สุดในแง่ของทั้งค่าเฉลี่ยเปอร์เซ็นต์ช่วงความเชื่อมั่นที่ครอบคลุมค่าสัมประสิทธิ์การถดถอยค่าจริงและค่าเฉลี่ยอัตราผลบวกเทียม บูตสแตรปที่ใช้สุ่มส่วนเหลือดีที่สุดในแง่ของค่าเฉลี่ยความกว้าง และบูตสแตรปที่ใช้สุ่มค่าถ่วงน้ำหนักดีที่สุดในแง่ของค่าเฉลี่ยอัตราผลลบเทียม

Statistical Analysis Of Demographic Effects On Insurance Coverage Of Perinatal And Neonatal Morbidity, Madeline Durbin

Undergraduate Honors Thesis Projects

In the United States of America, Ohio has one of the worst neonatal and perinatal death rates. Within Ohio, Montgomery County has an above average neonatal and perinatal death rate. This statistic can be lowered if more women in Montgomery County have health insurance. They would be more likely to seek out prenatal health care, since they would no longer have to pay as much money out-of-pocket. This would allow medical professionals to be able to diagnose and treat any potential issues in the mother or child earlier. Having health insurance would also prevent mothers-to-be from seeking out other potentially …

Natural Lead-In Approaches To Response-Adaptive Allocation In Clinical Trials, Erin E. Donahue

Theses and Dissertations

Response-adaptive (RA) allocation designs can be implemented in clinical trials to skew the allocation of incoming subjects toward the better performing treatment group based on the previously accrued subjects' responses. These designs alleviate potential ethical concerns of equally allocating subjects in a trial when one treatment arm is inferior. The RA design can be generalized to include covariate information in the covariate-adjusted response-adaptive (CARA) design, which aims to maximize treatment successes conditional on a set of patient characteristics. While RA and CARA designs can improve the treatment of patients, they have unstable estimators and increased variability in early stages of …

Phenotype Extraction: Estimation And Biometrical Genetic Analysis Of Individual Dynamics, Kevin L. Mckee

Theses and Dissertations

Within-person data can exhibit a virtually limitless variety of statistical patterns, but it can be difficult to distinguish meaningful features from statistical artifacts. Studies of complex traits have previously used genetic signals like twin-based heritability to distinguish between the two. This dissertation is a collection of studies applying state-space modeling to conceptualize and estimate novel phenotypic constructs for use in psychiatric research and further biometrical genetic analysis. The aims are to: (1) relate control theoretic concepts to health-related phenotypes; (2) design statistical models that formally define those phenotypes; (3) estimate individual phenotypic values from time series data; (4) consider hierarchical …

Estimating Response Status In Sequential Multiple Assignment (Smar)-Like Trials, Keighly Bradbrook

Theses and Dissertations

Sequential, multiple assignment, randomized trials (SMARTs) allow investigators to develop and compare experimental treatment regimens in which individuals are successively randomized to different treatments based on some set of predetermined rules. The rules used to make decisions on when and how to switch treatments are based on a chosen set of tailoring variables. Although not always true, intermediate response is commonly used as the primary tailoring variable as it is often predictive of future treatment success. As such, successful implementation depends on identifying patients who respond to treatment, though in some situations such mechanisms may not exist. Further, patient-level covariates …

The Analysis Of Neural Heterogeneity Through Mathematical And Statistical Methods, Kyle Wendling

Theses and Dissertations

Diversity of intrinsic neural attributes and network connections is known to exist in many areas of the brain and is thought to significantly affect neural coding. Recent theoretical and experimental work has argued that in uncoupled networks, coding is most accurate at intermediate levels of heterogeneity. I explore this phenomenon through two distinct approaches: a theoretical mathematical modeling approach and a data-driven statistical modeling approach.

Through the mathematical approach, I examine firing rate heterogeneity in a feedforward network of stochastic neural oscillators utilizing a high-dimensional model. The firing rate heterogeneity stems from two sources: intrinsic (different individual cells) and network …

Sex And Age Differences In Prevalence And Risk Factors For Prediabetes In Mexican-Americans, Kristina Vatcheva, Belinda M. Reininger, Susan P. Fisher-Hoch, Joseph B. Mccormick

School of Mathematical & Statistical Sciences Faculty Publications

AIMS:

Over 1/3 of Americans have prediabetes, while 9.4% have type 2 diabetes. The aim of our study was to estimate the prevalence of prediabetes in Mexican Americans, with known 28.2% prevalence of type 2 diabetes, by age and sex and to identify critical socio-demographic and clinical factors associated with prediabetes.

METHODS:

Data were collected between 2004 and 2017 from the Cameron County Hispanic Cohort in Texas. Weighted crude and sex- and age- stratified prevalences were calculated. Survey weighted logistic regression analyses were conducted to identify risk factors for prediabetes.

RESULTS:

The prevalence of prediabetes (32%) was slightly higher than …

An Empirical Comparison Of Machine Learning Models For Classification, Nubaira Rizvi

Theses and Dissertations

Classification problems are tackled across various industries throughout multiple disciplines. A model used for classification attempts to predict the class of an outcome variable based on some predictors. There are number of classification models available. But as the underlying population distribution of the predictors is always unknown it is difficult to know which model fits the situation best. Several studies have been done on which supervised model performs better given specific datasets. But little work has been done to compare the models’ performance for predicting one or more outcomes under multivariate settings.

This study compares the performance of seven popular …