Predicting Diabetes Diagnoses, 2020 Misericordia University
Predicting Diabetes Diagnoses, Sarah Netchert
Student Research Poster Presentations 2020
This study explored the traits and health state of African Americans in central Virginia in order to determine what traits put people at a higher probability of being diagnosed with diabetes. We also want to know which traits will generate the highest probability a person will be diagnosed with diabetes. Traits that were included and used in this study were cholesterol, stabilized glucose, high density lipoprotein levels, age(years), gender, height(inches), weight(pounds), systolic blood pressure, diastolic blood pressure, waist size(inches), and hip size(inches). There were 403 individuals included in study since they were only ones screened ...
Phenotype Extraction: Estimation And Biometrical Genetic Analysis Of Individual Dynamics, 2020 Virginia Commonwealth University
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 ...
Shrinkage Priors For Isotonic Probability Vectors And Binary Data Modeling, 2020 The University Of Michigan
Shrinkage Priors For Isotonic Probability Vectors And Binary Data Modeling, Philip S. Boonstra, Daniel R. Owen, Jian Kang
The University of Michigan Department of Biostatistics Working Paper Series
This paper outlines a new class of shrinkage priors for Bayesian isotonic regression modeling a binary outcome against a predictor, where the probability of the outcome is assumed to be monotonically non-decreasing with the predictor. The predictor is categorized into a large number of groups, and the set of differences between outcome probabilities in consecutive categories is equipped with a multivariate prior having support over the set of simplexes. The Dirichlet distribution, which can be derived from a normalized cumulative sum of gamma-distributed random variables, is a natural choice of prior, but using mathematical and simulation-based arguments, we show that ...
Artfima Processes And Their Applications To Solar Flare Data, 2020 Iowa State University
Artfima Processes And Their Applications To Solar Flare Data, Jinu Kabala
In this work, we demonstrate the application of ARTFIMA models on stable data derived from solar flare soft x-ray emissions. We study the solar flare data during a period of solar minimum which occurred most recently in July, August and September 2017. We use a two-state Hidden Markov Model to extract shorter stationary trajectories from the solar flare time series and classifying it into two states. In this work, we also introduce the ARTFIMA-GARCH model to model some of the trajectories. We do an end-to-end analysis, modeling and prediction of the solar flare data using both ARFIMA and ARTFIMA-GARCH models ...
Assessing Robustness Of The Rasch Mixture Model To Detect Differential Item Functioning - A Monte Carlo Simulation Study, Jinjin Huang
Electronic Theses and Dissertations
Measurement invariance is crucial for an effective and valid measure of a construct. Invariance holds when the latent trait varies consistently across subgroups; in other words, the mean differences among subgroups are only due to true latent ability differences. Differential item functioning (DIF) occurs when measurement invariance is violated. There are two kinds of traditional tools for DIF detection: non-parametric methods and parametric methods. Mantel Haenszel (MH), SIBTEST, and standardization are examples of non-parametric DIF detection methods. The majority of parametric DIF detection methods are item response theory (IRT) based. Both non-parametric methods and parametric methods compare differences among subgroups ...
Evaluating An Ordinal Output Using Data Modeling, Algorithmic Modeling, And Numerical Analysis, 2020 Murray State University
Evaluating An Ordinal Output Using Data Modeling, Algorithmic Modeling, And Numerical Analysis, Martin Keagan Wynne Brown
Murray State Theses and Dissertations
Data and algorithmic modeling are two diﬀerent approaches used in predictive analytics. The models discussed from these two approaches include the proportional odds logit model (POLR), the vector generalized linear model (VGLM), the classiﬁcation and regression tree model (CART), and the random forests model (RF). Patterns in the data were analyzed using trigonometric polynomial approximations and Fast Fourier Transforms. Predictive modeling is used frequently in statistics and data science to ﬁnd the relationship between the explanatory (input) variables and a response (output) variable. Both approaches prove advantageous in diﬀerent cases depending on the data set. In our case, the data ...
Variance Estimation After Kernel Ridge Regression Imputation, 2020 Iowa State University
Variance Estimation After Kernel Ridge Regression Imputation, Hengfang Wang, Jae Kwang Kim
Statistics Conference Proceedings, Presentations and Posters
Imputation is a popular technique for handling missing data. Variance estimation after imputation is an important practical problem in statistics. In this paper, we consider variance estimation of the imputed mean estimator under the kernel ridge regression imputation. We consider a linearization approach which employs the covariate balancing idea to estimate the inverse of propensity scores. The statistical guarantee of our proposed variance estimation is studied when a Sobolev space is utilized to do the imputation, where n-consistency can be obtained. Synthetic data experiments are presented to conﬁrm our theory.
Power Analysis On A Pilot Study Of The Caloric Intake Of Children Helping Prepare Meals Versus Children Not, 2020 Misericordia University
Power Analysis On A Pilot Study Of The Caloric Intake Of Children Helping Prepare Meals Versus Children Not, Danielle Clifford
Student Research Poster Presentations 2020
The purpose of this analysis is to determine the sample size needed for a study that will be used to discover if there is a difference in the caloric intake of children who help with meal preparation and children who do not help with meal preparation.
A Dynamic Model Of U.S. Beef Cattle, 2020 Iowa State University
A Dynamic Model Of U.S. Beef Cattle, Dinesh R. Poddaturi, Chad E. Hart, Lee L. Schulz, Sébastien Pouliot
Economics Presentations, Posters and Proceedings
We develop a simple and tractable model that will ultimately estimate and project prices and quantities in the U.S. beef cattle industry. While the economic literature focuses on static equilibrium displacement models to measure the impacts of policy proposals, the present study develops a dynamic model that includes cattle of all ages, price expectations, and demand for meat. In the cattle market, dynamics is an important feature, and absence of dynamics could lead to biased long-run estimates. By including dynamics we directly use data to estimate prices and quantities rather than relying on static counterfactuals. First we present and ...
Methodological Issues Of Spatial Agent-Based Models, 2020 University of Minnesota - Twin Cities
Methodological Issues Of Spatial Agent-Based Models, Steven Manson, Li An, Keith C. Clarke, Alison Heppenstall, Jennifer Koch, Brittany Krzyzanowski, Fraser Morgan, David O'Sullivan, Bryan C. Runck, Eric Shook, Leigh Tesfatsion
Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe ...
An Examination Of Covid-19 Statistical Modeling, 2020 The University of Akron
An Examination Of Covid-19 Statistical Modeling, Shane Vaughan
Williams Honors College, Honors Research Projects
The 2019 novel coronavirus, also known as COVID-19, is an infectious disease which was first reported in late 2019 and soon spread to become a global pandemic, prompting major action from world governments. Soon after, many institutions began attempts to analyze and predict the spread and severity of the disease via statistical modeling. Some information is not available for public consumption; however, a number of institutions have published the results of their analyses and some have made public repositories of the code used to build the models. This research paper attempts use these and other resources to examine the modeling ...
Accuracy Of Avs Life Expectancy Reports, 2020 The University of Akron
Accuracy Of Avs Life Expectancy Reports, Ariya Aghababa
Williams Honors College, Honors Research Projects
Use insurance company data to predict the trends in life insurance life expectancy reports. Also, use the data to predict what impairments could potentially decrease or increase an insured's life expectancy based on reports created by various Actuaries at life settlement companies.
The Analysis Of Neural Heterogeneity Through Mathematical And Statistical Methods, 2020 Virginia Commonwealth University
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 ...
A Mathematical Model For Malaria With Age-Heterogeneous Biting Rate, 2020 Minnesota State University, Mankato
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 ...
Conformal Prediction Intervals For Neural Networks Using Cross Validation, 2020 Iowa State University
Conformal Prediction Intervals For Neural Networks Using Cross Validation, Saeed Khaki
Neural networks are among the most powerful nonlinear models used to address supervised learning problems. Similar to most machine learning algorithms, neural networks produce point predictions and do not provide any prediction interval which includes an unobserved response value with a specified probability. In this creative component, we propose the k-fold prediction interval method to construct prediction intervals for neural networks based on k-fold cross validation. Simulation studies and analysis of 10 real datasets are used to compare the finite-sample properties of the prediction intervals produced by the proposed method and the split conformal (SC) method. The results suggest that ...
The Ritas Algorithm: A Constructive Yield Monitor Data Processing Algorithm, 2020 Iowa State University
The Ritas Algorithm: A Constructive Yield Monitor Data Processing Algorithm, Luis Damiano
Yield monitor datasets are known to contain a high percentage of unreliable records. The current tool set is mostly limited to observation cleaning procedures based on heuristic or empirically-motivated statistical rules for extreme value identification and removal. We propose a constructive algorithm for handling well-documented yield monitor data artifacts without resorting to data deletion. The four-step Rectangle creation, Intersection assignment and Tessellation, Apportioning, and Smoothing (RITAS) algorithm models sample observations as overlapping, unequally-shaped, irregularly-sized, time-ordered, areal spatial units to better replicate the nature of the destructive sampling process. Positional data is used to create rectangular areal spatial units. Time-ordered intersecting ...
Modeling The Galactic Compact Binary Neutron Star Population And Studying The Double Pulsar System, 2020 West Virginia University
Modeling The Galactic Compact Binary Neutron Star Population And Studying The Double Pulsar System, Nihan Pol
Graduate Theses, Dissertations, and Problem Reports
Binary neutron star (BNS) systems consisting of at least one neutron star provide an avenue for testing a broad range of physical phenomena ranging from tests of General Relativity to probing magnetospheric physics to understanding the behavior of matter in the densest environments in the Universe. Ultra-compact BNS systems with orbital periods less than few tens of minutes emit gravitational waves with frequencies ~mHz and are detectable by the planned space-based Laser Interferometer Space Antenna (LISA), while merging BNS systems produce a chirping gravitational wave signal that can be detected by the ground-based Laser Interferometer Gravitational-Wave Observatory (LIGO). Thus, BNS ...
Aggregate Loss Model With Poisson-Tweedie Loss Frequency, 2020 Wilfrid Laurier University
Aggregate Loss Model With Poisson-Tweedie Loss Frequency, Si Chen
Theses and Dissertations (Comprehensive)
The aggregate loss model has applications in various areas such as financial risk management and actuarial science. The aggregate loss is the summation of all random losses occurred in a period, and it is governed by both the loss severity and the loss frequency. While the impact of the loss severity on aggregate loss is well studied, less focus is paid on the influence of loss frequency on aggregate loss, which motivates our study. In this thesis, we enrich the aggregate loss framework by introducing the Poisson-Tweedie distribution as a candidate for modelling loss frequency, prove the closedness of Poisson-Tweedie ...
Semiparametric Fractional Imputation Using Gaussian Mixture Models For Handling Multivariate Missing Data, Hejian Sang, Jae Kwang Kim, Danhyang Lee
Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach of imputation for handling missing data. However, the parametric fractional imputation of Kim (2011) may be subject to bias under model misspecification. In this paper, we propose a novel semiparametric fractional imputation method using Gaussian mixture models. The proposed method is computationally efficient and leads to robust estimation. The proposed method is further extended to incorporate the categorical auxiliary information. The asymptotic model consistency and √n- consistency of the semiparametric fractional imputation estimator are also established ...
Accounting For Model Uncertainty In Multiple Imputation Under Complex Sampling, 2020 Kansas State University
Accounting For Model Uncertainty In Multiple Imputation Under Complex Sampling, Gyuhyeong Goh, Jae Kwang Kim
Multiple imputation provides an effective way to handle missing data. When several possible models are under consideration for the data, the multiple imputation is typically performed under a single-best model selected from the candidate models. This single model selection approach ignores the uncertainty associated with the model selection and so leads to underestimation of the variance of multiple imputation estimator. In this paper, we propose a new multiple imputation procedure incorporating model uncertainty in the final inference. The proposed method incorporates possible candidate models for the data into the imputation procedure using the idea of Bayesian Model Averaging (BMA). The ...