An Improved Two Independent-Samples Randomization Test For Single-Case Ab-Type Intervention Designs: A 20-Year Journey,
2020
University of Arizona
An Improved Two Independent-Samples Randomization Test For Single-Case Ab-Type Intervention Designs: A 20-Year Journey, Joel R. Levin, John M. Ferron, Boris S. Gafurov
Journal of Modern Applied Statistical Methods
Detailed is a 20-year arduous journey to develop a statistically viable two-phase (AB) single-case two independent-samples randomization test procedure. The test is designed to compare the effectiveness of two different interventions that are randomly assigned to cases. In contrast to the unsatisfactory simulation results produced by an earlier proposed randomization test, the present test consistently exhibited acceptable Type I error control under various design and effect-type configurations, while at the same time possessing adequate power to detect moderately sized intervention-difference effects. Selected issues, applications, and a multiple-baseline extension of the two-sample test are discussed.
Support Vector Machine-Based Modified Sp Statistic For Subset Selection With Non-Normal Error Terms,
2020
Department of Statistics, Gopal Krishna Gokhale College, Kolhapur (MS), India.
Support Vector Machine-Based Modified Sp Statistic For Subset Selection With Non-Normal Error Terms, Shivaji Shripati Desai, D N. Kashid
Journal of Modern Applied Statistical Methods
Support vector machine (SVM) is used for estimation of regression parameters to modify the sum of cross products (Sp). It works well for some nonnormal error distributions. The performance of existing robust methods and the modified Sp is evaluated through simulated and real data. The results show the performance of the modified Sp is good.
Flexible Box-Cox Transformation Model For Analyzing Energy Usage At Uconn,
2020
University of Connecticut
Flexible Box-Cox Transformation Model For Analyzing Energy Usage At Uconn, Yutong Chen
Honors Scholar Theses
The Box-Cox transformation is a way to transform non-normal data into more normally distributed data. However, when we fit linear regression models to transformed data, we cannot use the Akaike Information Criterion (AIC) directly to compare different models since the transformed data are no longer on the same scale. In this study, the Jacobian adjusted AIC is proposed to compare regression models on transformed data and to select an “optimal” value of the transformation parameter. Instead of using a single for the whole data, which is commonly used in the literature and in practice, we formulate a linear regression pattern …
Statistical Analysis Of Land Cover Conversion Trends In Northwest Ohio,
2020
Bowling Green State University
Statistical Analysis Of Land Cover Conversion Trends In Northwest Ohio, Chaska Mcgowan
Honors Projects
Agricultural land in the U.S. is abundant but not infinite. Change in cropland impacts national and local economies and the natural environment. The Black Swamp Conservancy (BSC), a non-profit land trust in Perrysburg, Ohio, is committed to preserving agricultural and natural lands in the Northwest Ohio region for future generations. This project was designed in collaboration with the BSC to illustrate the spatial distribution of land cover change within their sixteen-county service area in Northwest Ohio and to find a list of factors associated with land cover change in the region. The primary data source was the National Land Cover …
Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation.,
2020
University of Louisville
Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya
Electronic Theses and Dissertations
Generalized linear models have broad applications in biostatistics and sociology. In a regression setup, the main target is to find a relevant set of predictors out of a large collection of covariates. Sparsity is the assumption that only a few of these covariates in a regression setup have a meaningful correlation with an outcome variate of interest. Sparsity is incorporated by regularizing the irrelevant slopes towards zero without changing the relevant predictors and keeping the resulting inferences intact. Frequentist variable selection and sparsity are addressed by popular techniques like Lasso, Elastic Net. Bayesian penalized regression can tackle the curse of …
Using Stability To Select A Shrinkage Method,
2020
University of Nebraska-Lincoln
Using Stability To Select A Shrinkage Method, Dean Dustin
Department of Statistics: Dissertations, Theses, and Student Research
Shrinkage methods are estimation techniques based on optimizing expressions to find which variables to include in an analysis, typically a linear regression. The general form of these expressions is the sum of an empirical risk plus a complexity penalty based on the number of parameters. Many shrinkage methods are known to satisfy an ‘oracle’ property meaning that asymptotically they select the correct variables and estimate their coefficients efficiently. In Section 1.2, we show oracle properties in two general settings. The first uses a log likelihood in place of the empirical risk and allows a general class of penalties. The second …
Introduction To Research Statistical Analysis: An Overview Of The Basics,
2020
HCA Healthcare
Introduction To Research Statistical Analysis: An Overview Of The Basics, Christian Vandever
HCA Healthcare Journal of Medicine
This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.
Logistic Growth Modeling With Markov Chain Monte Carlo Estimation,
2020
The George Washington University
Logistic Growth Modeling With Markov Chain Monte Carlo Estimation, Jaehwa Choi, Jinsong Chen, Jeffrey R. Harring
Journal of Modern Applied Statistical Methods
A new growth modeling approach is proposed to can fit inherently nonlinear (i.e., logistic) function without constraint nor reparameterization. A simulation study is employed to investigate the feasibility and performance of a Markov chain Monte Carlo method within Bayesian estimation framework to estimate a fully random version of a logistic growth curve model under manipulated conditions such as the number and timing of measurement occasions and sample sizes.
A Simulation Study On Increasing Capture Periods In Bayesian Closed Population Capture-Recapture Models With Heterogeneity,
2020
Appalachian State University
A Simulation Study On Increasing Capture Periods In Bayesian Closed Population Capture-Recapture Models With Heterogeneity, Ross M. Gosky, Joel Sanqui
Journal of Modern Applied Statistical Methods
Capture-Recapture models are useful in estimating unknown population sizes. A common modeling challenge for closed population models involves modeling unequal animal catchability in each capture period, referred to as animal heterogeneity. Inference about population size N is dependent on the assumed distribution of animal capture probabilities in the population, and that different models can fit a data set equally well but provide contradictory inferences about N. Three common Bayesian Capture-Recapture heterogeneity models are studied with simulated data to study the prevalence of contradictory inferences is in different population sizes with relatively low capture probabilities, specifically at different numbers of …
484— Modeling Social Distancing Methods And Their Effectiveness In Combating The Spread Of Ebola,
2020
SUNY Geneseo
484— Modeling Social Distancing Methods And Their Effectiveness In Combating The Spread Of Ebola, Rachel Fair
GREAT Day Posters
Ebola Virus Disease (EVD) is a rare but severe disease that is transmitted among humans through direct-contact with, and close proximity to, infected bodily fluids. From 2014-16, West Africa experienced the largest Ebola outbreak ever recorded, infecting over 28,000 people, and killing over 11,000. Although the symptoms of EVD are treatable, the disease can be extremely deadly, with an average of 50% EVD cases resulting in fatality. In areas where healthcare is scarce and vaccinations are not readily available, the practices of social distancing and self-quarantining have been shown to be highly effective in combating the spread of EVD. To …
Universal Vector Neural Machine Translation With Effective Attention,
2020
SMU
Universal Vector Neural Machine Translation With Effective Attention, Joshua Yi, Satish Mylapore, Ryan Paul, Robert Slater
SMU Data Science Review
Neural Machine Translation (NMT) leverages one or more trained neural networks for the translation of phrases. Sutskever intro- duced a sequence to sequence based encoder decoder model which be- came the standard for NMT based systems. Attention mechanisms were later introduced to address the issues with the translation of long sen- tences and improving overall accuracy. In this paper, we propose two improvements to the encoder decoder based NMT approach. Most trans- lation models are trained as one model for one translation. We introduce a neutral/universal model representation that can be used to predict more than one language depending on …
An Exploration Of Link Functions Used In Ordinal Regression,
2020
Northern Illinois University
An Exploration Of Link Functions Used In Ordinal Regression, Thomas J. Smith, David A. Walker, Cornelius M. Mckenna
Journal of Modern Applied Statistical Methods
The purpose of this study is to examine issues involved with choice of a link function in generalized linear models with ordinal outcomes, including distributional appropriateness, link specificity, and palindromic invariance are discussed and an exemplar analysis provided using the Pew Research Center 25th anniversary of the Web Omnibus Survey data. Simulated data are used to compare the relative palindromic invariance of four distinct indices of determination/discrimination, including a newly proposed index by Smith et al. (2017).
Analysis Of Gas Mileage Of A Car,
2020
Georgia College
Analysis Of Gas Mileage Of A Car, Joshua Ballard-Myer
Georgia College Student Research Events
The objective of this work is to analyze a data set, Auto, from the R package ISLR: Introduction to Statistical Learning in R. The data set includes information for 392 observations on 9 variables including gas mileage, horsepower, weight in pounds, and engine displacement in cubic inches. The data set was taken from the StatLib library maintained at Carnegie Mellon University. The primary response variable will be gas mileage in miles per gallon, with all other variables serving as predictors, but other relationships with other response variables such as acceleration will be explored. Results were similar to expected; traits desirable …
Personal Foul: How Head Trauma And The Insurance Industry Are Threatening Sports,
2020
Liberty University
Personal Foul: How Head Trauma And The Insurance Industry Are Threatening Sports, Zachary Cooler
Senior Honors Theses
This thesis will investigate the growing problem of head trauma in contact sports like football, hockey, and soccer through medical studies, implications to the insurance industry, and ongoing litigation. The thesis will investigate medical studies that are finding more evidence to support the claim that contact sports players are more likely to receive head trauma symptoms such as memory loss, mood swings, and even Lou Gehrig’s disease in extreme cases. The thesis will also demonstrate that these medical symptoms and monetary losses from medical claims are convincing insurance companies to withdraw insurance coverage for sports leagues, which they are justifying …
Quasi-Likelihood Ratio Tests For Homoscedasticity In Linear Regression,
2020
Georgia Southern University
Quasi-Likelihood Ratio Tests For Homoscedasticity In Linear Regression, Lili Yu, Varadan Sevilimedu, Robert Vogel, Hani Samawi
Journal of Modern Applied Statistical Methods
Two quasi-likelihood ratio tests are proposed for the homoscedasticity assumption in the linear regression models. They require few assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests.
Investigating The Performance Of Propensity Score Approaches For Differential Item Functioning Analysis,
2020
University of British Columbia
Investigating The Performance Of Propensity Score Approaches For Differential Item Functioning Analysis, Yan Liu, Chanmin Kim, Amrey D. Wu, Paul Gustafson, Edward Kroc, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
To evaluate the performance of propensity score approaches for differential item functioning analysis, this simulation study was conducted to assess bias, mean square error, Type I error, and power under different levels of effect size and a variety of model misspecification conditions, including different types and missing patterns of covariates.
A Glossary On Building Longitudinal, Population-Based Data Linkages To Explore Children’S Developmental Trajectories,
2020
University of British Columbia
A Glossary On Building Longitudinal, Population-Based Data Linkages To Explore Children’S Developmental Trajectories, Jennifer E. V. Lloyd, Jacqui Boonstra, Lisa Chen, Barry Forer, Ruth Hershler, Constance Milbrath, Brenda T. Poon, Neda Razaz, Pippa Rowcliffe, Kimberly Schonert-Reichl
Journal of Modern Applied Statistical Methods
Population-based, person-specific, longitudinal child and youth health and developmental data linkages involve connecting combinations of specially-collected data and administrative data for longitudinal population research purposes. This glossary provides definitions of key terms and concepts related to their theoretical basis, research infrastructure, research methodology, statistical analysis, and knowledge translation.
Robust Confidence Intervals For The Population Mean Alternatives To The Student-T Confidence Interval,
2020
The Hashemite University, Zarqa, Jordan
Robust Confidence Intervals For The Population Mean Alternatives To The Student-T Confidence Interval, Moustafa Omar Ahmed Abu-Shawiesh, Aamir Saghir
Journal of Modern Applied Statistical Methods
In this paper, three robust confidence intervals are proposed as alternatives to the Student‑t confidence interval. The performance of these intervals was compared through a simulation study shows that Qn-t confidence interval performs the best and it is as good as Student’s‑t confidence interval. Real-life data was used for illustration and performing a comparison that support the findings obtained from the simulation study.
Using Spss To Analyze Complex Survey Data: A Primer,
2020
University of British Columbia
Using Spss To Analyze Complex Survey Data: A Primer, Danjie Zou, Jennifer E. V. Lloyd, Jennifer L. Baumbusch
Journal of Modern Applied Statistical Methods
An introduction to using SPSS to analyze complex survey data is given. Key features of complex survey design are described briefly, including stratification, clustering, multiple stages, and weights. Then, annotated SPSS syntax for complex survey data analysis is presented to demonstrate the step-by-step process using real complex samples data.
D-Vine Copula Model For Dependent Binary Data,
2020
Old Dominion University
D-Vine Copula Model For Dependent Binary Data, Huihui Lin, N. Rao Chaganty
College of Sciences Posters
High-dimensional dependent binary data are prevalent in a wide range of scientific disciplines. A popular method for analyzing such data is the Multivariate Probit (MP) model. But the MP model sometimes fails even within a feasible range of binary correlations, because the underlying correlation matrix of the latent variables may not be positive definite. In this research, we proposed pair copula models, assuming the dependence between the binary variables is first order autoregressive (AR(1))or equicorrelated structure. Also, when Archimediean copula is used, most paper converted Kendall Tau to corresponding copula parameter, there is no explicit function of Pearson’s correlation coefficient …
