Statistical Models Commons^™

Analysis Of 2016-17 Major League Soccer Season Data Using Poisson Regression With R, Ian D. Campbell

Undergraduate Theses and Capstone Projects

To the outside observer, soccer is chaotic with no given pattern or scheme to follow, a random conglomeration of passes and shots that go on for 90 minutes. Yet, what if there was a pattern to the chaos, or a way to describe the events that occur in the game quantifiably. Sports statistics is a critical part of baseball and a variety of other of today’s sports, but we see very little statistics and data analysis done on soccer. Of this research, there has been looks into the effect of possession time on the outcome of a game, the difference …

Cognitive Virtual Admissions Counselor, Kumar Raja Guvindan Raju, Cory Adams, Raghuram Srinivas

SMU Data Science Review

Abstract. In this paper, we present a cognitive virtual admissions counselor for the Master of Science in Data Science program at Southern Methodist University. The virtual admissions counselor is a system capable of providing potential students accurate information at the time that they want to know it. After the evaluation of multiple technologies, Amazon’s LEX was selected to serve as the core technology for the virtual counselor chatbot. Student surveys were leveraged to collect and generate training data to deploy the natural language capability. The cognitive virtual admissions counselor platform is currently capable of providing an end-to-end conversational dialog to …

Developing Methods Of Processing And Analyzing Genetic Data To Examine Tiger Salamander Population Structure, Dennis Dongmin Kim

Undergraduate Research Symposium 2018

Professor Heather Waye and her colleagues conducted a pilot study in 2014 to measure genetic diversity and dispersal pattern in a population of tiger salamanders in west-central Minnesota. The ultimate goal of this research was to analyze the genetic differences between tiger salamander larvae captured in breeding ponds within Pepperton Waterfowl Production Area to understand the population structure and movement patterns. They expected that ponds closer to each other would have more similar genetic information, and that genetic differences between ponds would increase with geographic distance. However, the initial analysis using standard techniques failed to uncover useful patterns in the …

Developing Statistical Methods For Data From Platforms Measuring Gene Expression, Gaoxiang Jia

Statistical Science Theses and Dissertations

This research contains two topics: (1) PBNPA: a permutation-based non-parametric analysis of CRISPR screen data; (2) RCRnorm: an integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data from FFPE samples.

Clustered regularly-interspaced short palindromic repeats (CRISPR) screens are usually implemented in cultured cells to identify genes with critical functions. Although several methods have been developed or adapted to analyze CRISPR screening data, no single spe- cific algorithm has gained popularity. Thus, rigorous procedures are needed to overcome the shortcomings of existing algorithms. We developed a Permutation-Based Non-Parametric Analysis (PBNPA) algorithm, which computes p-values at the gene level …

Analysis Of A Voting Method For Ranking Network Centrality Measures On A Node-Aligned Multiplex Network, Kyle S. Wilkinson

Theses and Dissertations

Identifying relevant actors using information gleaned from multiple networks is a key goal within the context of human aspects of military operations. The application of a voting theory methodology for determining nodes of critical importance—in ranked order of importance—for a node-aligned multiplex network is demonstrated. Both statistical and qualitative analyses on the differences of ranking outcomes under this methodology is provided. As a corollary, a multilayer network reduction algorithm is investigated within the context of the proposed ranking methodology. The application of the methodology detailed in this thesis will allow meaningful rankings of relevant actors to be produced on a …

Modeling Multimodal Failure Effects Of Complex Systems Using Polyweibull Distribution, Daniel A. Timme

Theses and Dissertations

The Department of Defense (DoD) enlists multiple complex systems across each of their departments. Between the aging systems going through an overhaul and emerging new systems, quality assurance to complete the mission and secure the nation‘s objectives is an absolute necessity. The U.S. Air Force‘s increased interest in Remotely Piloted Aircraft (RPA) and the Space Warfighting domain are current examples of complex systems that must maintain high reliability and sustainability in order to complete missions moving forward. DoD systems continue to grow in complexity with an increasing number of components and parts in more complex arrangements. Bathtub-shaped hazard functions arise …

Default Priors For The Intercept Parameter In Logistic Regressions, Philip S. Boonstra, Ryan P. Barbaro, Ananda Sen

The University of Michigan Department of Biostatistics Working Paper Series

In logistic regression, separation refers to the situation in which a linear combination of predictors perfectly discriminates the binary outcome. Because finite-valued maximum likelihood parameter estimates do not exist under separation, Bayesian regressions with informative shrinkage of the regression coefficients offer a suitable alternative. Little focus has been given on whether and how to shrink the intercept parameter. Based upon classical studies of separation, we argue that efficiency in estimating regression coefficients may vary with the intercept prior. We adapt alternative prior distributions for the intercept that downweight implausibly extreme regions of the parameter space rendering less sensitivity to separation. …

Incorporating Historical Models With Adaptive Bayesian Updates, Philip S. Boonstra, Ryan P. Barbaro

The University of Michigan Department of Biostatistics Working Paper Series

This paper considers Bayesian approaches for incorporating information from a historical model into a current analysis when the historical model includes only a subset of covariates currently of interest. The statistical challenge is two-fold. First, the parameters in the nested historical model are not generally equal to their counterparts in the larger current model, neither in value nor interpretation. Second, because the historical information will not be equally informative for all parameters in the current analysis, additional regularization may be required beyond that provided by the historical information. We propose several novel extensions of the so-called power prior that adaptively …

Essentials Of Structural Equation Modeling, Mustafa Emre Civelek

Zea E-Books Collection

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, Steven Hornyak

National Youth Advocacy & Resilience Conference

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.

New Approaches To Model Simulated Spatio-Temporal Moran's Index, Nhan Bu, Jennifer Lorio, Norou Diawara, Kumar Das, Lance Waller

Mathematics & Statistics Faculty Publications

The Moran's index is a statistic that measures spatial autocorrelation; it quantifies the degree of dispersion (or clustering) of objects in space. However, when investigating data over a general area, a single global Moran statistic may not give a sufficient summary of the spread, behavior, features or latent surfaces shared by neighboring areas; rather, by partitioning the area and taking the Moran statistic of each divided subareas, we can discover patterns of the local neighbors not otherwise apparent. In this paper, we present a simulation experiment where the local Moran values are computed and a time variable is added to …

Models As Weapons: Review Of Weapons Of Math Destruction: How Big Data Increases Inequality And Threatens Democracy By Cathy O’Neil (2016), Samuel L. Tunstall

Numeracy

Cathy O’Neil. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (New York, NY: Crown) 272 pp. ISBN 978-0553418811.

Accessible to a wide readership, Cathy O’Neil’s Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy provides a lucid yet alarming account of the extensive reach of mathematical models in influencing all of our lives. With a particular eye towards social justice, O’Neil not only warns modelers to be cognizant of the effects of their work on real people—especially vulnerable groups who have less power to fight back—but also encourages laypersons to take initiative …

The Impact Of Truncating Data On The Predictive Ability For Single-Step Genomic Best Linear Unbiased Prediction, Jeremy T. Howard, Thomas A. Rathje, Caitlyn E. Bruns, Danielle F. Wilson-Wells, Stephen D. Kachman, Matthew L. Spangler

Department of Animal Science: Faculty Publications

Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single-step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively …

Effect Of Socioeconomic And Demographic Factors On Kentucky Crashes, Aaron Berry Cambron

Theses and Dissertations--Civil Engineering

The goal of this research was to examine the potential predictive ability of socioeconomic and demographic data for drivers on Kentucky crash occurrence. Identifying unique background characteristics of at-fault drivers that contribute to crash rates and crash severity may lead to improved and more specific interventions to reduce the negative impacts of motor vehicle crashes. The driver-residence zip code was used as a spatial unit to connect five years of Kentucky crash data with socioeconomic factors from the U.S. Census, such as income, employment, education, age, and others, along with terrain and vehicle age. At-fault driver crash counts, normalized over …

Accounting For Spatial Autocorrelation In Modeling The Distribution Of Water Quality Variables, Lorrayne Miralha

Theses and Dissertations--Geography

Several studies in hydrology have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this thesis, I hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. I selected ten watersheds in the USA and analyzed them to determine whether water quality variables with higher Moran’s I values undergo greater increases in the coefficient of determination (R²) and greater decreases in residual SAC (rSAC) after spatial modeling. I compared non-spatial ordinary …

Improved Methods And Selecting Classification Types For Time-Dependent Covariates In The Marginal Analysis Of Longitudinal Data, I-Chen Chen

Theses and Dissertations--Epidemiology and Biostatistics

Generalized estimating equations (GEE) are popularly utilized for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, when certain types of time-dependent covariates are presented, these equations can be biased unless an independence working correlation structure is employed. Moreover, in this case regression parameter estimation can be very inefficient because not all valid moment conditions are incorporated within the corresponding estimating equations. Therefore, approaches using the generalized method of moments or quadratic inference functions have been proposed for utilizing all valid moment conditions. However, we have found that …

Accounting For Matching Uncertainty In Photographic Identification Studies Of Wild Animals, Amanda R. Ellis

Theses and Dissertations--Statistics

I consider statistical modelling of data gathered by photographic identification in mark-recapture studies and propose a new method that incorporates the inherent uncertainty of photographic identification in the estimation of abundance, survival and recruitment. A hierarchical model is proposed which accepts scores assigned to pairs of photographs by pattern recognition algorithms as data and allows for uncertainty in matching photographs based on these scores. The new models incorporate latent capture histories that are treated as unknown random variables informed by the data, contrasting past models having the capture histories being fixed. The methods properly account for uncertainty in the matching …

The Family Of Conditional Penalized Methods With Their Application In Sufficient Variable Selection, Jin Xie

Theses and Dissertations--Statistics

When scientists know in advance that some features (variables) are important in modeling a data, then these important features should be kept in the model. How can we utilize this prior information to effectively find other important features? This dissertation is to provide a solution, using such prior information. We propose the Conditional Adaptive Lasso (CAL) estimates to exploit this knowledge. By choosing a meaningful conditioning set, namely the prior information, CAL shows better performance in both variable selection and model estimation. We also propose Sufficient Conditional Adaptive Lasso Variable Screening (SCAL-VS) and Conditioning Set Sufficient Conditional Adaptive Lasso Variable …

Mixtures-Of-Regressions With Measurement Error, Xiaoqiong Fang

Theses and Dissertations--Statistics

Finite Mixture model has been studied for a long time, however, traditional methods assume that the variables are measured without error. Mixtures-of-regression model with measurement error imposes challenges to the statisticians, since both the mixture structure and the existence of measurement error can lead to inconsistent estimate for the regression coefficients. In order to solve the inconsistency, We propose series of methods to estimate the mixture likelihood of the mixtures-of-regressions model when there is measurement error, both in the responses and predictors. Different estimators of the parameters are derived and compared with respect to their relative efficiencies. The simulation results …

Estimation In Partially Linear Models With Correlated Observations And Change-Point Models, Liangdong Fan

Theses and Dissertations--Statistics

Methods of estimating parametric and nonparametric components, as well as properties of the corresponding estimators, have been examined in partially linear models by Wahba [1987], Green et al. [1985], Engle et al. [1986], Speckman [1988], Hu et al. [2004], Charnigo et al. [2015] among others. These models are appealing due to their flexibility and wide range of practical applications including the electricity usage study by Engle et al. [1986], gum disease study by Speckman [1988], etc., wherea parametric component explains linear trends and a nonparametric part captures nonlinear relationships.

The compound estimator (Charnigo et al. [2015]) has been used to …