Multivariate Spectral Analysis Of Crism Data To Characterize The Composition Of Mawrth Vallis, 2018 Wesleyan University
Multivariate Spectral Analysis Of Crism Data To Characterize The Composition Of Mawrth Vallis, Melissa Luna
No abstract provided.
Essentials Of Structural Equation Modeling, 2018 Istanbul Commerce University
Essentials Of Structural Equation Modeling, Mustafa Emre Civelek
Structural Equation Modeling is a statistical method increasingly used in scientific studies in the fields of Social Sciences. It is currently a preferred analysis method, especially in doctoral dissertations and academic researches. However, since many universities do not include this method in the curriculum of undergraduate and graduate courses, students and scholars try to solve the problems they encounter by using various books and internet resources.
This book aims to guide the researcher who wants to use this method in a way that is free from math expressions. It teaches the steps of a research program using structured equality modeling ...
Building A Better Risk Prevention Model, 2018 Houston County Schools
Building A Better Risk Prevention Model, Steven Hornyak
National Youth-At-Risk Conference Savannah
This presentation chronicles the work of Houston County Schools in developing a risk prevention model built on more than ten years of longitudinal student data. In its second year of implementation, Houston At-Risk Profiles (HARP), has proven effective in identifying those students most in need of support and linking them to interventions and supports that lead to improved outcomes and significantly reduces the risk of failure.
Optimal Stratification And Allocation For The June Agricultural Survey, 2018 Iowa State University
Optimal Stratification And Allocation For The June Agricultural Survey, Cigna, Hejian Sang, Zhengyuan Zhu, Stephanie Zimmer
A computational approach to optimal multivariate designs with respect to stratification and allocation is investigated under the assumptions of fixed total allocation, known number of strata, and the availability of administrative data correlated with thevariables of interest under coefficient-of-variation constraints. This approach uses a penalized objective function that is optimized by simulated annealing through exchanging sampling units and sample allocations among strata. Computational speed is improved through the use of a computationally efficient machine learning method such as K-means to create an initial stratification close to the optimal stratification. The numeric stability of the algorithm has been investigated and parallel ...
Modelling The Common Risk Among Equities Using A New Time Series Model, 2018 The University of Western Ontario
Modelling The Common Risk Among Equities Using A New Time Series Model, Jingjia Chu
Electronic Thesis and Dissertation Repository
A new additive structure of multivariate GARCH model is proposed where the dynamic changes of the conditional correlation between the stocks are aggregated by the common risk term. The observable sequence is divided into two parts, a common risk term and an individual risk term, both following a GARCH type structure. The conditional volatility of each stock will be the sum of these two conditional variance terms. All the conditional volatility of the stock can shoot up together because a sudden peak of the common volatility is a sign of the system shock.
We provide sufficient conditions for strict stationarity ...
Prediction Intervals For Functional Data, 2018 Montclair State University
Prediction Intervals For Functional Data, Nicholas Rios
Theses, Dissertations and Culminating Projects
The prediction of functional data samples has been the focus of several functional data analysis endeavors. This work describes the use of dynamic function-on-function regression for dynamic prediction of the future trajectory as well as the construction of dynamic prediction intervals for functional data. The overall goals of this thesis are to assess the efficacy of Dynamic Penalized Function-on-Function Regression (DPFFR) and to compare DPFFR prediction intervals with those of other dynamic prediction methods. To make these comparisons, metrics are used that measure prediction error, prediction interval width, and prediction interval coverage. Simulations and applications to financial stock data from ...
Partially Linear Functional Additive Models For Multivariate Functional Data, 2018 Texas A&M University
Partially Linear Functional Additive Models For Multivariate Functional Data, Raymond K.W. Wong, Yehua Li, Zhengyuan Zhu
We investigate a class of partially linear functional additive models (PLFAM) that predicts a scalar response by both parametric effects of a multivariate predictor and nonparametric effects of a multivariate functional predictor. We jointly model multiple functional predictors that are cross-correlated using multivariate functional principal component analysis (mFPCA), and model the nonparametric effects of the principal component scores as additive components in the PLFAM. To address the high dimensional nature of functional data, we let the number of mFPCA components diverge to infinity with the sample size, and adopt the COmponent Selection and Smoothing Operator (COSSO) penalty to select relevant ...
Cellulose Nanofiber-Reinforced Impact Modified Polypropylene: Assessing Material Properties From Fused Layer Modeling And Injection Molding Processing, Jordan Elliott Sanders
Electronic Theses and Dissertations
The purpose of this research was to investigate the use of cellulose nanofibers (CNF) compounded into an impact modified polypropylene (IMPP) matrix. A IMPP was used because it shrinks less than a PP homopolymer during FLM processing. An assessment of material properties from fused layer modeling (FLM), an additive manufacturing (AM) method, and injection molding (IM) was conducted. Results showed that material property measurements in neat PP were statistically similar between IM and FLM for density, strain at yield and flexural stiffness. Additionally, PP plus the coupling agent maleic anhydride (MA) showed statistically similar results in comparison of IM and ...
Making Models With Bayes, 2017 California State University, San Bernardino
Making Models With Bayes, Pilar Olid
Electronic Theses, Projects, and Dissertations
Bayesian statistics is an important approach to modern statistical analyses. It allows us to use our prior knowledge of the unknown parameters to construct a model for our data set. The foundation of Bayesian analysis is Bayes' Rule, which in its proportional form indicates that the posterior is proportional to the prior times the likelihood. We will demonstrate how we can apply Bayesian statistical techniques to fit a linear regression model and a hierarchical linear regression model to a data set. We will show how to apply different distributions to Bayesian analyses and how the use of a prior affects ...
Variational Bayes Estimation Of Time Series Copulas For Multivariate Ordinal And Mixed Data, 2017 Melbourne Business School
Variational Bayes Estimation Of Time Series Copulas For Multivariate Ordinal And Mixed Data, Ruben Loaiza-Maya, Michael S. Smith
Michael Stanley Smith
On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, 2017 The University of Western Ontario
On The Estimation Of Penetrance In The Presence Of Competing Risks With Family Data, Daniel Prawira
Electronic Thesis and Dissertation Repository
In family studies, we are interested in estimating the penetrance function of the event of interest in the presence of competing risks. Failure to account for competing risks may lead to bias in the estimation of the penetrance function. In this thesis, three statistical challenges are addressed: clustering, missing data, and competing risks. We proposed the cause-specific model with shared frailty and ascertainment correction to account for clustering and competing risks along with ascertainment of families into study. Multiple imputation is used to account for missing data. The simulation study showed good performance of our proposed model in estimating the ...
A Cross-Sectional Exploration Of Household Financial Reactions And Homebuyer Awareness Of Registered Sex Offenders In A Rural, Suburban, And Urban County., John Charles Navarro
Electronic Theses and Dissertations
As stigmatized persons, registered sex offenders betoken instability in communities. Depressed home sale values are associated with the presence of registered sex offenders even though the public is largely unaware of the presence of registered sex offenders. Using a spatial multilevel approach, the current study examines the role registered sex offenders influence sale values of homes sold in 2015 for three U.S. counties (rural, suburban, and urban) located in Illinois and Kentucky within the social disorganization framework. Homebuyers were surveyed to examine whether awareness of local registered sex offenders and the homebuyer’s community type operate as moderators between ...
Burden Of Atopic Dermatitis In The United States: Analysis Of Healthcare Claims Data In The Commercial, Medicare, And Medi-Cal Databases, 2017 STATinMED Research/SIMR, Inc.
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.
The Importance Of Inhaler Technique In Measuring And Calculating Inhaler Adherence, And Its Clinical Outcomes, 2017 Royal College of Surgeons in Ireland
The Importance Of Inhaler Technique In Measuring And Calculating Inhaler Adherence, And Its Clinical Outcomes, Imran Sulaiman
Depending on the population studied, cross-sectional observational studies suggest that between 14%-90% of patients do not use their pressurized metered dose inhaler correctly, while 50-60% misuse a dry powder inhaler. This means that unless incorrect technique is acounted for a significant underestimation of how much medication the person actually obtained may be made.
The aim of this thesis was to objectively determine the frequency and importance of inhaler technique errors and to combine these with inhaler use to provide an acurate method of calculating adherence. I then investigated different patterns of inhaler use, determinants of inhaler use and the ...
An Investigation Of The Accuracy Of Parallel Analysis For Determining The Number Of Factors In A Factor Analysis, 2017 Western Kentucky University
An Investigation Of The Accuracy Of Parallel Analysis For Determining The Number Of Factors In A Factor Analysis, Mandy Matsumoto
Honors College Capstone Experience/Thesis Projects
Exploratory factor analysis is an analytic technique used to determine the number of factors in a set of data (usually items on a questionnaire) for which the factor structure has not been previously analyzed. Parallel analysis (PA) is a technique used to determine the number of factors in a factor analysis. There are a number of factors that affect the results of a PA: the choice of the eigenvalue percentile, the strength of the factor loadings, the number of variables, and the sample size of the study. Although PA is the most accurate method to date to determine which factors ...
Marketing The Mountain State: A Large N Study Of User Engagement On Twitter, 2017 Illinois State University
Marketing The Mountain State: A Large N Study Of User Engagement On Twitter, Kirk Richardson
Capstone Projects – Politics and Government
Much of the evolving research on the use of social media in destination marketing emphasizes how information diffusion influences the reputational image of place. The present study uses Twitter data to focus on the relative differences in user engagement across discrete account types. Specifically, this is done to examine how the official destination marketing organization of Montana—the Montana Office of Tourism (MTOT)—performs relative to other account types. Several regression analyses conducted on Twitter data associated with an ongoing MTOT place branding campaign reveal that tweets sent from ‘official’ accounts are more likely to be retweeted, and are estimated ...
Detecting And Evaluating Therapy Induced Changes In Radiomics Features Measured From Non-Small Cell Lung Cancer To Predict Patient Outcomes, 2017 The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences
Detecting And Evaluating Therapy Induced Changes In Radiomics Features Measured From Non-Small Cell Lung Cancer To Predict Patient Outcomes, Xenia J. Fave
UT GSBS Dissertations and Theses (Open Access)
The purpose of this study was to investigate whether radiomics features measured from weekly 4-dimensional computed tomography (4DCT) images of non-small cell lung cancers (NSCLC) change during treatment and if those changes are prognostic for patient outcomes or dependent on treatment modality. Radiomics features are quantitative metrics designed to evaluate tumor heterogeneity from routine medical imaging. Features that are prognostic for patient outcome could be used to monitor tumor response and identify high-risk patients for adaptive treatment. This would be especially valuable for NSCLC due to the high prevalence and mortality of this disease.
A novel process was designed to ...
Performance Of Imputation Algorithms On Artificially Produced Missing At Random Data, 2017 East Tennessee State University
Performance Of Imputation Algorithms On Artificially Produced Missing At Random Data, Tobias O. Oketch
Electronic Theses and Dissertations
Missing data is one of the challenges we are facing today in modeling valid statistical models. It reduces the representativeness of the data samples. Hence, population estimates, and model parameters estimated from such data are likely to be biased.
However, the missing data problem is an area under study, and alternative better statistical procedures have been presented to mitigate its shortcomings. In this paper, we review causes of missing data, and various methods of handling missing data. Our main focus is evaluating various multiple imputation (MI) methods from the multiple imputation of chained equation (MICE) package in the statistical software ...
Modelling Cash Crop Growth In Tn, 2017 University of Tennessee
Modelling Cash Crop Growth In Tn, Spencer Weston
University of Tennessee Honors Thesis Projects
No abstract provided.
Statistically Analyzing Assembly Line Processing Times Through Incorporation Of Product Variation, 2017 Murray State University
Statistically Analyzing Assembly Line Processing Times Through Incorporation Of Product Variation, Kyle Rehr, Matthew Farr
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 ...