Categorical Data Analysis Commons^™

Improving The Accuracy For The Long-Term Hydrologic Impact Assessment (L-Thia) Model, Anqi Zhang, Lawrence Theller, Bernard A. Engel

The Summer Undergraduate Research Fellowship (SURF) Symposium

Urbanization increases runoff by changing land use types from less impervious to impervious covers. Improving the accuracy of a runoff assessment model, the Long-Term Hydrologic Impact Assessment (L-THIA) Model, can help us to better evaluate the potential uses of Low Impact Development (LID) practices aimed at reducing runoff, as well as to identify appropriate runoff and water quality mitigation methods. Several versions of the model have been built over time, and inconsistencies have been introduced between the models. To improve the accuracy and consistency of the model, the equations and parameters (primarily curve numbers in the case of this model ...

Burden Of Atopic Dermatitis In The United States: Analysis Of Healthcare Claims Data In The Commercial, Medicare, And Medi-Cal Databases, Sulena Shrestha, Raymond Miao, Li Wang, Jingdong Chao, Huseyin Yuce, Wenhui Wei

Publications and Research

Comparative data on the burden of atopic dermatitis (AD) in adults relative to the general population are limited. We performed a large-scale evaluation of the burden of disease among US adults with AD relative to matched non-AD controls, encompassing comorbidities, healthcare resource utilization (HCRU), and costs, using healthcare claims data. The impact of AD disease severity on these outcomes was also evaluated.

Mining Of Primary Healthcare Patient Data With Selective Multimorbid Diseases, Annette Megerdichian Azad

Electronic Thesis and Dissertation Repository

Despite a large volume of research on the prognosis, diagnosis and overall burden of multimorbidity, very little is known about socio-demographic characteristics of multimorbid patients. This thesis aims to analyze the socio-demographic characteristics of patients with multiple chronic conditions (multimorbidity), focusing on patient groups sharing the same combination of diseases. Several methods were explored to analyze the co-occurrence of multiple chronic diseases as well as the associations between socio-demographics and chronic conditions. These methods include disease pair distributions over gender, age groups and income level quintiles, Multimorbidity Coefficients for measuring the concurrence of disease pairs and triples, and k-modes clustering ...

Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim

Research Collection School Of Information Systems

Social media, as a major platform to disseminate information, has changed the way users and communities contribute content. In this paper, we aim to study content modifications on public Facebook pages operated by news media, community groups, and bloggers. We also study the possible reasons behind them, and their effects on user interaction. We conducted a detailed study of Content Censorship (CC) and Content Edit (CE) in Facebook using a detailed longitudinal dataset consisting of 57 public Facebook pages over 3 weeks covering 145,955 posts and 9,379,200 comments. We detected many CC and CE activities between 28 ...

Statistically Analyzing Assembly Line Processing Times Through Incorporation Of Product Variation, Kyle Rehr, Matthew Farr

Scholars Week

Timing methods and performance metrics are important in the heavily industrialized world we live in. Industrial plants use metrics to measure quality of production, help make decisions, and drive the strategy of the organization. However, there are many factors to be considered when measuring performance based on a metric; of which we will be analyzing the importance of product variation. We will be analyzing assembly line timings, whilst controlling for product variance, to show the importance differences between products makes in one’s ability to predict performance. In addition, we will be analyzing the current “statistical” methods used by an ...

Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang

Research Collection School Of Information Systems

The discrete Fréchet distance (DFD) captures perceptual and geographicalsimilarity between discrete trajectories. It has been successfullyadopted in a multitude of applications, such as signatureand handwriting recognition, computer graphics, as well as geographicapplications. Spatial applications, e.g., sports analysis,traffic analysis, etc. require discovering the pair of most similarsubtrajectories, be them parts of the same or of different input trajectories.The identified pair of subtrajectories is called a motif.The adoption of DFD as the similarity measure in motif discovery,although semantically ideal, is hindered by the high computationalcomplexity of DFD calculation. In this paper, we propose asuite of novel lower ...

2014 Reporting Of Sexual Assault: Institutional Comparisons, M. E. Karns

Research Studies and Reports

Institutions of higher education are required to submit annual reports of sexual assault crimes to the Department of Education under the Clery Act. The Department of Education makes this data publicly available. Two primary measures are used to assess reporting of assault on campus: the Assault Reporting Ratio (ARR) and the Reporting Rate per 10,000 students (R10K). These measures are easily calculated and can be used to assess practices and policies that impact the reporting of sexual assault on campus.

The ARR and R10K are rate comparisons, a method widely used in public health. These rate comparisons measure how ...

The Value Of A Collegiate Far Part 141 Jeopardy-Crew Resource Management (Crm)-Simulation Event, Samuel M. Vance

Journal of Aviation/Aerospace Education & Research

This article explores the viability of using a FAR Part 141 collegiate crew resource management (CRM) flight simulator scenario event as a jeopardy event (a graded, syllabus item) in an upper-level professional pilot curriculum course. Ultimately, the objective is to suggest this approach as a value-added curriculum consideration for other collegiate professional pilot programs. The selection of four CRM criteria to be examined was made by the course professor. Using the four principles, the students assembled the grading rubric for their event. The simulator scenario placed students in airspace, geography and weather dissimilar to that in which they were training ...

Utilizing Tumor Exome Variation To Predict Cancer Treatment Outcomes, Michael Rendleman

University of Iowa Honors Theses

Cancer genomics, in the context of informing clinical decisions with tumor genotype, is a field characterized by high-dimensional data. Computational approaches for evaluating sets of features to be utilized in machine learning methods are essential for yielding accurate predictive and prognostic models. Additionally, the publicly-available results of the Broad Institute’s Firehose cancer genomics analysis pipeline presents a wealth of information that may be useful for cancer genotyping. Power analysis and classifier comparison are performed with the goal of evaluating a gene-based mutation significance feature set (MutSig) from Firehose. They reveal that while the MutSig features likely contain some prognostic ...

Quantifying The Effect Of The Shift In Major League Baseball, Christopher John Hawke Jr.

Senior Projects Spring 2017

Baseball is a very strategic and abstract game, but the baseball world is strangely obsessed with statistics. Modern mainstream statisticians often study offensive data, such as batting average or on-base percentage, in order to evaluate player performance. However, this project observes the game from the opposite perspective: the defensive side of the game. In hopes of analyzing the game from a more concrete perspective, countless mathemeticians - most famously, Bill James - have developed numerous statistical models based on real life data of Major League Baseball (MLB) players. Large numbers of metrics go into these models, but what this project attempts to ...

A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz

Doctor of Business Administration Dissertations

At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with ...

Effects Of Prescribed Fire On The Forest Structure And Composition At Land Between The Lakes National Recreation Area, Ky, Miranda Thompson

Honors College Theses

With a regular fire regime present on the landscape, open canopies and herbaceous understories characterize oak forests in western Kentucky. However, a long period of fire suppression has changed the structure and composition of many forests in the Southeast. Forest managers at Land Between the Lakes have started using prescribed fire in an attempt to replicate aspects of a natural fire regime and increase the amount of open oak woodlands and savannas in the area. The prescribed fires in our study area were conducted during the dormant season and are very low intensity ground fires.

To understand how prescribed fire ...

Hilbe Mcd E-Book2016 Errata 03nov2016, Joseph M. Hilbe

Joseph M Hilbe

Calculating Odds Ratios From Probabillities, Joseph M. Hilbe

Joseph M Hilbe

Cost Sensitive Online Multiple Kernel Classification, Doyen Sahoo, Peilin Zhao, Hoi, Steven C. H.

Research Collection School Of Information Systems

Learning from data streams has been an important open research problem in the era ofbig data analytics. This paper investigates supervised machine learning techniques formining data streams with application to online anomaly detection. Unlike conventionalmachine learning tasks, machine learning from data streams for online anomaly detectionhas several challenges: (i) data arriving sequentially and increasing rapidly, (ii) highlyclass-imbalanced distributions; and (iii) complex anomaly patterns that could evolve dynamically.To tackle these challenges, we propose a novel Cost-Sensitive Online MultipleKernel Classification (CSOMKC) scheme for comprehensively mining data streams anddemonstrate its application to online anomaly detection. Specifically, CSOMKC learns akernel-based cost-sensitive prediction model ...

Converting A Logistic Model Odds Ratio To A Risk Ratio, Joseph M. Hilbe

Joseph M Hilbe

Hilbe Mcd Errata 03nov2016 Update, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data; Errata And Additions

Joseph M Hilbe

Modeling Count Data Rcode Update 15sep2016, Joseph M. Hilbe

Joseph M Hilbe

Hilbe-Mcd-Stata-Code-Revised-15sep2016.Pdf, Joseph M. Hilbe

Joseph M Hilbe