Statistics and Probability Commons^™

Provision Of Hospital-Based Palliative Care And The Impact On Organizational And Patient Outcomes, Marisa L. Roczen

Theses and Dissertations

Hospital-based palliative care services aim to streamline medical care for patients with chronic and potentially life-limiting illnesses by focusing on individual patient needs, efficient use of hospital resources, and providing guidance for patients, patients’ families and clinical providers toward making optimal decisions concerning a patient’s care. This study examined the nature of palliative care provision in U.S. hospitals and its impact on selected organizational and patient outcomes, including hospital costs, length of stay, in-hospital mortality, and transfer to hospice. Hospital costs and length of stay are viewed as important economic indicators. Specifically, lower hospital costs may increase a hospital’s profit …

Non-Conventional Approaches To Syntheses Of Ferromagnetic Nanomaterials, Dustin M. Clifford

Theses and Dissertations

The work of this dissertation is centered on two non-conventional synthetic approaches to ferromagnetic nanomaterials: high-throughput experimentation (HTE) (polyol process) and continuous flow (CF) synthesis (aqueous reduction and the polyol process). HTE was performed to investigate phase control between Fe_xCo_1-x and Co_3-xFe_xO_y. Exploration of synthesis limitations based on magnetic properties was achieved by reproducing M_s=210 emu/g. Morphological control of Fe_xCo_1-x alloy was achieved by formation of linear chains using an H_ext. The final study of the Fe_xCo_1-x chains used DoE to …

Modeling Spatially Varying Effects Of Chemical Mixtures, Jenna Czarnota

Theses and Dissertations

Cancer incidence is associated with exposures to multiple environmental chemicals, and geographic variation in cancer rates suggests the importance of accommodating spatially varying effects in the analysis of environmental chemical mixtures and disease risk. Traditional regression methods are challenged by the complex correlation patterns inherent among co-occurring chemicals, and the applicability of geographically weighted regression models is limited in the setting of environmental chemical risk analysis. In comparison to traditional methods, weighted quantile sum (WQS) regression performs well in the identification of important environmental exposures, but is limited by the assumption that effects are fixed over space. We present an …

In-Shoe Plantar Pressure System To Investigate Ground Reaction Force Using Android Platform, Ahmed A. Mostfa

Theses and Dissertations

Human footwear is not yet designed to optimally relieve pressure on the heel of the foot. Proper foot pressure assessment requires personal training and measurements by specialized machinery. This research aims to investigate and hypothesize about Preferred Transition Speed (PTS) and to classify the gait phase of explicit variances in walking patterns between different subjects. An in-shoe wearable pressure system using Android application was developed to investigate walking patterns and collect data on Activities of Daily Living (ADL). In-shoe circuitry used Flexi-Force A201 sensors placed at three major areas: heel contact, 1st metatarsal, and 5th metatarsal with a PIC16F688 microcontroller …

Selecting Spatial Scale Of Area-Level Covariates In Regression Models, Lauren Grant

Theses and Dissertations

Studies have found that the level of association between an area-level covariate and an outcome can vary depending on the spatial scale (SS) of a particular covariate. However, covariates used in regression models are customarily modeled at the same spatial unit. In this dissertation, we developed four SS model selection algorithms that select the best spatial scale for each area-level covariate. The SS forward stepwise, SS incremental forward stagewise, SS least angle regression (LARS), and SS lasso algorithms allow for the selection of different area-level covariates at different spatial scales, while constraining each covariate to enter at most one spatial …

Dimension Reduction And Variable Selection, Hossein Moradi Rekabdarkolaee

Theses and Dissertations

High-dimensional data are becoming increasingly available as data collection technology advances. Over the last decade, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics, signal processing, and environmental studies. Statistical techniques such as dimension reduction and variable selection play important roles in high dimensional data analysis. Sufficient dimension reduction provides a way to find the reduced space of the original space without a parametric model. This method has been widely applied in many scientific fields such as genetics, brain imaging analysis, econometrics, environmental sciences, etc. …

Early Sex Work Initiation And Condom Use Among Alcohol-Using Female Sex Workers In Mombasa, Kenya: A Cross-Sectional Analysis, A. M. Parcesepe, Kelly L'Engle, S. L. Martin, S. Green, C. Suchindran, P. Mwarogo

Nursing and Health Professions Faculty Research and Publications

Objectives Early initiation of sex work is prevalent among female sex workers (FSWs) worldwide. The objectives of this study were to investigate if early initiation of sex work was associated with: (1) consistent condom use, (2) condom negotiation self-efficacy or (3) condom use norms among alcohol-using FSWs in Mombasa, Kenya.

Methods In-person interviews were conducted with 816 FSWs in Mombasa, Kenya. Sample participants were: recruited from HIV prevention drop-in centres, 18 years or older and moderate risk drinkers. Early initiation was defined as first engaging in sex work at 17 years or younger. Logistic regression modelled outcomes as a function …

The Relationship Between Exercise And Depression And Anxiety In College Students, Joshua Frank, Dr. Amy Adkins, Nathan Thomas, Dr. Danielle Dick

UROP Posters

The literature shows an inverse association between exercise and mental disorders. The aim of this study is to further elaborate on this association with regards to exercise and its relationship with anxiety and depression in a college sample. The subject group focused on seniors in the Spit for Science data set which incorporated a total of 821 students. Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ) to estimate the overall metabolic equivalents (MET’s) each student spent in walking, moderate, or vigorous activity levels in the previous week. Sum scores were used to measure depression and anxiety. Overall,the …

An Analysis Of Accuracy Using Logistic Regression And Time Series, Edwin Baidoo, Jennifer L. Priestley

Published and Grey Literature from PhD Candidates

This paper analyzes the accuracy rates for logistic regression and time series models. It also examines a relatively new performance index that takes into consideration the business assumptions of credit markets. Although prior research has focused on evaluation metrics, such as AUC and Gini index, this new measure has a more intuitive interpretation for various managers and decision makers and can be applied to both Logistic and Time Series models.

A Comparison Of Machine Learning Techniques And Logistic Regression Method For The Prediction Of Past-Due Amount, Jie Hao, Jennifer L. Priestley

Published and Grey Literature from PhD Candidates

The aim of this paper to predict a past-due amount using traditional and machine learning techniques: Logistic Analysis, k-Nearest Neighbor and Random Forest. The dataset to be analyzed is provided by Equifax, which contains 305 categories of financial information from more than 11,787,287 unique businesses from 2006 to 2014. The big challenge is how to handle with the big and noisy real world datasets. Among the three techniques, the results show that Logistic Regression Method is the best in terms of predictive accuracy and type I errors.

Application Of Isotonic Regression In Predicting Business Risk Scores, Linh T. Le, Jennifer L. Priestley

Published and Grey Literature from PhD Candidates

An isotonic regression model fits an isotonic function of the explanatory variables to estimate the expectation of the response variable. In other words, as the function increases, the estimated expectation of the response must be non-decreasing. With this characteristic, isotonic regression could be a suitable option to analyze and predict business risk scores. A current challenge of isotonic regression is the decrease of performance when the model is fitted in a large data set e.g. more than four or five dimensions. This paper attempts to apply isotonic regression models into prediction of business risk scores using a large data set …

The Smallest Non-Autograph, Benjamin Baumer, Yijin Wei, Gary S. Bloom

Mathematics Sciences: Faculty Publications

Suppose that G is a simple, vertex-labeled graph and that S is a multiset. Then if there exists a one-to-one mapping between the elements of S and the vertices of G, such that edges in G exist if and only if the absolute difference of the corresponding vertex labels exist in S, then G is an autograph, and S is a signature for G. While it is known that many common families of graphs are autographs, and that infinitely many graphs are not autographs, a non-autograph has never been exhibited. In this paper, we identify the smallest non-autograph: a graph …

Pre-Tuned Principal Component Regression And Several Variants, Pei Wang

Open Access Theses & Dissertations

The regression coecient estimates from ordinary least squares (OLS) have a low probability of being close to the real value when there is a multicollinearity problem in the design matrix. In order to combat this problem, many regularized methods have been introduced. Principal components regression (PCR) is an important analysis tool for dealing with multicollinearity and high-dimensionality. In conventional PCR, the rst step is to change the original predictors to orthogonal principal components (PC's) by a linear transformation. These PC's correspond to the eigenvalues which are sorted in a decreasing order. The next step is to regress the response on …

Bayesian Parameter Estimation For The Birnbaum-Saunders Distribution And Its Extension, Tun Lee Ng

Open Access Theses & Dissertations

We utilize the Bayesian approach to estimate the parameters of the Birnbaum-Saunders (BS) distribution devised by Birnbaum and Saunders (1969a), as well as the Generalized Birnbaum-Saunders (GBS) distribution obtained by Owen (2006), in the presence of random right censored data. We also derive the classical MLE expressions for the observed Information matrix of the GBS distribution, in order to illustrate the fact that no closed form expressions are available for the MLE, and numerical approximations are required to obtain the point estimates and asymptotic confidence intervals. Where Bayesian approach is concerned, new sets of priors are considered based on the …

Diversification And Market Neutral Portfolios In S&P500, Alan S. Agnew

Williams Honors College, Honors Research Projects

Our goal is to investigate strategies to deal with the risks associated with holding asset in the stock market. We first deal with risk of holding a specific stock, by the use of diversification. Later, we’ll attempt to deal with the market risk, which is the risk of entire market going up and down. Data used in this project comes from daily adjusted closing price of stocks listed in the S&P500 index ranging from January 3^rd, 2000 to December 31^st, 2015 and the data is processed using statistical software R.

Sections 2 through 4 of this …

Comparison Of Option Price From Black-Scholes Model To Actual Values, Matthew J. Krznaric

Williams Honors College, Honors Research Projects

The Black-Scholes model is a widely used method for pricing European-style options in a straightforward way, through the use of calculations and ideal market assumptions. Due to certain unrealistic ideal conditions exercised by the model, The Black-Scholes technique of pricing options may not be entirely accurate in implementation. This paper addresses these problems due to the model limitations, determining how The Black-Scholes method compares to the results when using the actual data. Using a mix of historical S&P500 data and generated normal distributions, we first calculated and graphed option prices through the Black-Scholes formulas. With the help of R, we …

Simulation Of Nuclear Fusion Using A One Dimensional Particle In Cell Method, Steven T. Margell

Cal Poly Humboldt theses and projects

In this thesis several novel techniques are developed to simulate fusion events in an isotropic, electrostatic three-dimensional Deuterium-Tritium plasma. These techniques allow us to accurately predict three-dimensional collision events with a one-dimensional model while simultaneously reducing compute time via a nearest neighbor algorithm. Furthermore, a fusion model based on first principles is developed that yields an average fusion reactivity which correlates well with empirical results.

Macroconstants Of Development: A New Benchmark For The Strategic Development Of Advanced Countries And Firms, Andrey V. Bystrov, Vyacheslav N. Yusim, Tamilla Curtis

Publications

This research proposed a new indicator of countries’ development called “macroconstants of development”. The literature review indicates that the concept of "macroconstants of development" is not used at the moment in neither the theory nor the practice of industrial policy. Research of longitudinal data of total GDP, GDP per capita and their derivatives for most countries of the world was conducted. An analysis of statistical information has been done by employing econometric analyses.

Based on the analysis of the statistical data, which characterizes the development of large, technologically advanced countries in ordinary conditions, it was identified that the average acceleration …

Fact Sheet, Yale University Office Of Institutional Research

Fact Sheets

No abstract provided.

A Hierarchical Statistical Engineering Modeling Methodology, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

In the ASEM-IAC 2015, Cotter (2015) proposed a systemic joint deterministic-stochastic dynamic causal Bayesian statistical engineering model that addressed the knowledge gap needed to integrate deterministic mathematical engineering models within a stochastic framework. However, Cotter did not specify the modeling methodology through which statistical engineering models could be developed, diagnosed, and applied to predict systemic mission performance. This paper updates research into the development a hierarchical statistical engineering modeling methodology and sets forth the initial theoretical foundation for the methodology.