Applied Statistics Commons^™

A Framework For Inferring Unobserved Multistrain Epidemic Subpopulations Using Synchronization Dynamics, Eric Forgoston, Leah B. Shaw, Ira B. Schwartz

Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works

A new method is proposed to infer unobserved epidemic subpopulations by exploiting the synchronization properties of multistrain epidemic models. A model for dengue fever is driven by simulated data from secondary infective populations. Primary infective populations in the driven system synchronize to the correct values from the driver system. Most hospital cases of dengue are secondary infections, so this method provides a way to deduce unobserved primary infection levels. We derive center manifold equations that relate the driven system to the driver system and thus motivate the use of synchronization to predict unobserved primary infectives. Synchronization stability between primary and …

Mapping Open Water Bodeis With Optical Remote Sensing, Mary Ellen O'Donnell, Erika Podest

STAR Program Research Presentations

There is interest in mapping open water bodies using remote sensing data. Coverage and persistence of open water is currently a poorly measured variable due to its spatial and temporal variability across landscapes, especially in remote areas. The presence and persistence of open water is one of the primary indicators of conditions suitable for mosquito breeding habitats. Predicting the risk of mosquito caused disease outbreaks is a required step towards their control and eradication. Satellite observations can provide needed data to support agency decisions for deployment of preventative measures and control resources. This study, which will try to map open …

Beta-Binomial Kriging: A New Approach To Modeling Spatially Correlated Proportions, Aimee Schwab

Department of Statistics: Dissertations, Theses, and Student Research

Spatially correlated count data sets appear often in applied data analysis problems, but there is little consensus in the literature about how best to analyze the data. The two prevailing approaches provide accurate parameter estimates and predictions, at the cost of model interpretability and simplicity. This dissertation will present a new approach to modeling spatially correlated binomial observations: beta-binomial kriging. The model proposed here is a modified form of spatial kriging which assumes the data are generated from a correlated beta-binomial distribution. Given this assumption, the spatial parameters and predicted values can be estimated using simple matrix algebra. Beta-binomial kriging …

Review Of Developing Quantitative Literacy Skills In History And The Social Sciences: A Web-Based Common Core Approach By Kathleen W. Craver, Victor J. Ricchezza, H L. Vacher

Numeracy

Kathleen W. Craver. Developing Quantitative Literacy Skills in History and Social Sciences: A Web-Based Common Core Standards Approach (Lantham MD: Rowman & Littlefield Publishing Group, Inc., 2014). 191 pp.
ISBN 978-1-4758-1050-9 (cloth); ISBN …-1051-6 (pbk); ISBN…-1052-3 (electronic).

This book could be a breakthrough for teachers in the trenches who are interested in or need to know about quantitative literacy (QL). It is a resource providing 85 topical pieces, averaging 1.5 pages, in which a featured Web site is presented, described, and accompanied by 2-4 critical-thinking questions purposefully drawing on data from the Web site. The featured Web sites range from …

Penalized Function-On-Function Regression, Andrada Ivanescu, Ana Maria Staicu, Fabian Scheipl, Sonja Greven

Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works

A general framework for smooth regression of a functional response on one or multiple functional predictors is proposed. Using the mixed model representation of penalized regression expands the scope of function-on-function regression to many realistic scenarios. In particular, the approach can accommodate a densely or sparsely sampled functional response as well as multiple functional predictors that are observed on the same or different domains than the functional response, on a dense or sparse grid, and with or without noise. It also allows for seamless integration of continuous or categorical covariates and provides approximate confidence intervals as a by-product of the …

A Study Of The Parametric And Nonparametric Linear-Circular Correlation Coefficient, Robin Tu

Statistics

Circular statistics are specialized statistical methods that deal specifically with directional data. Data that is angular require specialized techniques due to the modulo 2π (in radians) or modulo 360^◦ (in degrees) nature of angles.

Correlation, typically in terms of Pearson’s correlation coefficient, is a measure of association between two linear random variables x and y. In this paper, the specific circular technique of the parametric and nonparametric linear-circular correlation coefficient will be explored where correlation is no longer between two linear variables x and y, but between a linear random variable x and circular random variable θ.

A simulation …

#Twittercritic: Sentiment Analysis Of Tweets To Predict Tv Ratings, Isabel Litton

Statistics

Twitter has rapidly become one of the most popular sites of the Internet. It functions not just as a microblogging service, but as a crowdsourcing tool for listening, promotion, insight and much more. From the perspective of TV networks, tweets capture the real time reactions of viewers, making them an ideal indicator of a show’s ratings. This paper predicts Internet Movie Database (IMDB) television ratings by text mining Twitter data.

Tweets for five television shows were downloaded over a period of several months utilizing a SAS macro. Television show data, such as rating, show title, episode title, and more were …

Statistical Consulting - Senior Project, Cary Hernandez

Statistics

No abstract provided.

A Hierarchical Bayesian Model For The Unmixing Analysis Of Compositional Data Subject To Unit-Sum Constraints, Shiyong Yu

LSU New Orleans Theses and Dissertations

Modeling of compositional data is emerging as an active area in statistics. It is assumed that compositional data represent the convex linear mixing of definite numbers of independent sources usually referred to as end members. A generic problem in practice is to appropriately separate the end members and quantify their fractions from compositional data subject to nonnegative and unit-sum constraints. A number of methods essentially related to polytope expansion have been proposed. However, these deterministic methods have some potential problems.

In this study, a hierarchical Bayesian model was formulated, and the algorithms were coded in MATLAB^Ò. A test …

Video Event Understanding With Pattern Theory, Fillipe Souza, Sudeep Sarkar, Anuj Srivastava, Jingyong Su

MODVIS Workshop

We propose a combinatorial approach built on Grenander’s pattern theory to generate semantic interpretations of video events of human activities. The basic units of representations, termed generators, are linked with each other using pairwise connections, termed bonds, that satisfy predefined relations. Different generators are specified for different levels, from (image) features at the bottom level to (human) actions at the highest, providing a rich representation of items in a scene. The resulting configurations of connected generators provide scene interpretations; the inference goal is to parse given video data and generate high-probability configurations. The probabilistic structures are imposed using energies that …

Binocular 3d Motion Perception As Bayesian Inference, Martin Lages, Suzanne Heron

MODVIS Workshop

The human visual system encodes monocular motion and binocular disparity input before it is integrated into a single 3D percept. Here we propose a geometric-statistical model of human 3D motion perception that solves the aperture problem in 3D by assuming that (i) velocity constraints arise from inverse projection of local 2D velocity constraints in a binocular viewing geometry, (ii) noise from monocular motion and binocular disparity processing is independent, and (iii) slower motions are more likely to occur than faster ones. In two experiments we found that instantiation of this Bayesian model can explain perceived 3D line motion direction under …

Applying Penalized Binary Logistic Regression With Correlation Based Elastic Net For Variables Selection, Zakariya Yahya Algamal, Muhammad Hisyam Lee

Journal of Modern Applied Statistical Methods

Reduction of the high dimensional classification using penalized logistic regression is one of the challenges in applying binary logistic regression. The applied penalized method, correlation based elastic penalty (CBEP), was used to overcome the limitation of LASSO and elastic net in variable selection when there are perfect correlation among explanatory variables. The performance of the CBEP was demonstrated through its application in analyzing two well-known high dimensional binary classification data sets. The CBEP provided superior classification performance and variable selection compared with other existing penalized methods. It is a reliable penalized method in binary logistic regression.

Spss Programs For Addressing Two Forms Of Power For Multiple Regression Coefficients, Christopher Aberson

Journal of Modern Applied Statistical Methods

This paper presents power analysis tools for multiple regression. The first takes input of correlations between variables and sample size and outputs power for multiple predictors. The second addresses power to detect significant effects for all of the predictors in the model. Both employ user-friendly SPSS Custom Dialogs.

Are Per-Family Type I Error Rates Relevant In Social And Behavioral Science?, Andrew V. Frane

Journal of Modern Applied Statistical Methods

The familywise Type I error rate is a familiar concept in hypothesis testing, whereas the per‑family Type I error rate is rarely addressed. This article uses Monte Carlo simulations and graphics to make a case for the relevance of the per‑family Type I error rate in research practice and pedagogy.

Per Family Error Rates: A Response, James F. Troendle, Keshia-Lee Martin, Vance W. Berger

Journal of Modern Applied Statistical Methods

As the authors note, the familywise error rate (FWER) is used rather often, whereas the per-family error rate (PFER) is not. Is this as it should be? It would seem that no universal answer is possible, as context determines which is more appropriate in any given application. In the general scenario of testing the benefit of an intervention, one might ideally want an error rate that aligns with the decision for benefit. In most cases the FWER does this pretty well, while allowing one to identify those endpoints for which benefit exists. The PFER does not seem to have any …

Maximum Likelihood Estimation Of The Kumaraswamy Exponential Distribution With Applications, K. A. Adepoju, O. I. Chukwu

Journal of Modern Applied Statistical Methods

The Kumaraswamy exponential distribution, a generalization of the exponential, is developed as a model for problems in environmental studies, survival analysis and reliability. The estimation of parameters is approached by maximum likelihood and the observed information matrix is derived. The proposed models are applied to three real data sets.

Test For The Equality Of Partial Correlation Coefficients For Two Populations, Madhusudan Bhandary, Arjun K. Gupta

Journal of Modern Applied Statistical Methods

A likelihood ratio test for the equality of two partial correlation coefficients based on two independent multinormal samples has been derived. The large sample Z-test for the same problem has also been discussed. The power analysis of the two tests is obtained. It has been found that the approximate likelihood ratio (ALR) test showed consistently better results than Z -test in terms of power. The size of the ALR test is slightly more than the alpha level. The ALR test is recommended strongly for use in practice.

Comparison Of Model Fit Indices Used In Structural Equation Modeling Under Multivariate Normality, Sengul Cangur, Ilker Ercan

Journal of Modern Applied Statistical Methods

The purpose of this study is to investigate the impact of estimation techniques and sample sizes on model fit indices in structural equation models constructed according to the number of exogenous latent variables under multivariate normality. The performances of fit indices are compared by considering effects of related factors. The Ratio Chi-square Test Statistic to Degree of Freedom, Root Mean Square Error of Approximation, and Comparative Fit Index are the least affected indices by estimation technique and sample size under multivariate normality, especially with large sample size.

Method Of Estimation In The Presence Of Non-Response And Measurement Errors Simultaneously, Rajesh Singh Singh, Prayas Sharma

Journal of Modern Applied Statistical Methods

The problem of estimating the finite population mean of in simple random sampling in the presence of non-response and response error was considered. The estimators use auxiliary information to improve efficiency, assuming non–response and measurement error are present in both the study and auxiliary variables. A class of estimators was proposed and its properties studied in the simultaneous presence of non-response and response errors. It was shown that the proposed class of estimators is more efficient than the usual unbiased estimator, ratio and product estimators under non-response and response error together. A numerical study was carried out to compare its …

Pseudo-Random Number Generators For Vector Processors And Multicore Processors, Agner Fog

Journal of Modern Applied Statistical Methods

Large scale Monte Carlo applications need a good pseudo-random number generator capable of utilizing both the vector processing capabilities and multiprocessing capabilities of modern computers in order to get the maximum performance. The requirements for such a generator are discussed. New ways of avoiding overlapping subsequences by combining two generators are proposed. Some fundamental philosophical problems in proving independence of random streams are discussed. Remedies for hitherto ignored quantization errors are offered. An open source C++ implementation is provided for a generator that meets these needs.

Estimating The Accuracy Of Automated Classification Systems Using Only Expert Ratings That Are Less Accurate Than The System, Paul E. Lehner

Journal of Modern Applied Statistical Methods

A method is presented to estimate the accuracy of an automated classification system based only on expert ratings on test cases, where the system may be substantially more accurate than the raters. In this method an estimate of overall rater accuracy is derived from the level of inter-rater agreement, Bayesian updating based on estimated rater accuracy is applied to estimate a ground truth probability for each classification on each test case, and then overall system accuracy is estimated by comparing the relative frequency that the system agrees with the most probable classification at different probability levels. A simulation analysis provides …

Modeling Probability Of Causal And Random Impacts, Stan Lipovetsky, Igor Mandel

Journal of Modern Applied Statistical Methods

The method of the estimation of the probability of an event occurring under the influence of the causal and random effects is considered. Epistemological differences from the traditional approaches to causality are discussed, and a new model of the statistical estimation of the parameters of each effect is proposed. The simple and effective algorithms of the model parameters estimation are presented, and numerical simulations are performed. A practical marketing example is analyzed. The results support the validity of the estimation procedure and open the perspective for the application of the method for various decision making problems, where different causes can …

Estimation For The Parameters Of The Exponentiated Exponential Distribution Using A Median Ranked Set Sampling, Monjed H. Samuh, Areen Qtait

Journal of Modern Applied Statistical Methods

The method of maximum likelihood estimation based on Median Ranked Set Sampling (MRSS) was used to estimate the shape and scale parameters of the Exponentiated Exponential Distribution (EED). They were compared with the conventional estimators. The relative efficiency was used for comparison. The amount of information (in Fisher's sense) available from the MRSS about the parameters of the EED were be evaluated. Confidence intervals for the parameters were constructed using MRSS.

A Decision Support Tool For Appointment Scheduling To Reduce Patient No-Show Rate In An Outpatient Psychiatric Clinic, Kaitlyn N. Thomas

Industrial Engineering Undergraduate Honors Theses

The Walker Family Clinic in the Psychiatric Research Institute at the University of Arkansas for Medical Sciences in Little Rock, Arkansas provides general and specialty mental health and substance abuse services for adolescents and adults. As there is an increasing need for health services at the clinic, the current capacity may not be able to meet all demands. Patients may wait a long time before receiving care due to inefficiencies in the current system. Also, based on data collected from August 1, 2013 to November 26, 2014, the average daily no-show rate was 13.9% and the maximum daily no-show rate …

Estimating The Strength Of An Association Based On A Robust Smoother, Rand Wilcox

Journal of Modern Applied Statistical Methods

It is known that the more obvious parametric approaches to fitting a regression line to data are often not flexible enough to provide an adequate approximation of the true regression line. Many nonparametric regression estimators, often called smoothers, have been derived that are aimed at dealing with this problem. The paper deals with the issue of estimating the strength of an association based on the fit obtained by a robust smoother. A simple approach, already known, is to estimate explanatory power in a fairly obvious manner. This approach has been found to perform reasonably well when using the smoother LOESS. …

Per Family Or Familywise Type I Error Control: "Eether, Eyether, Neether, Nyther, Let's Call The Whole Thing Off!", H. J. Keselman

Journal of Modern Applied Statistical Methods

Frane (2015) pointed out the difference between per-family and familywise Type I error control and how different multiple comparison procedures control one method but not necessarily the other. He then went on to demonstrate in the context of a two group multivariate design containing different numbers of dependent variables and correlations between variables how the per-family rate inflates beyond the level of significance. In this article I reintroduce other newer better methods of Type I error control. These newer methods provide more power to detect effects than the per-family and familywise techniques of control yet maintain the overall rate of …

Comparison Of Bayesian Credible Intervals To Frequentist Confidence Intervals, Kathy Gray, Brittany Hampton, Tony Silveti-Falls, Allison Mcconnell, Casey Bausell

Journal of Modern Applied Statistical Methods

Frequentist confidence intervals were compared with Bayesian credible intervals under a variety of scenarios to determine when Bayesian credible intervals outperform frequentist confidence intervals. Results indicated that Bayesian interval estimation frequently produces results with precision greater than or equal to the frequentist method.

Special Education Distributions And Analysis, Valerie Felder, Shlomo S. Sawilowsky

Journal of Modern Applied Statistical Methods

Micceri (1989) examined the distributional characteristics of 440 large sample general education achievement and psychometric measures. All the distributions were found to be statistically significantly different from the normal distribution. In this study, 395 special education datasets were examined. Although there were some normally distributed datasets, most were not, and some were markedly different in shape from those found by Micceri (1989). Implications for statistical testing and making special education policy decisions were given.

Vol. 14, No. 1 (Full Issue), Jmasm Editors

Journal of Modern Applied Statistical Methods

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A Comparison Of Semi-Parametric And Nonparametric Methods For Estimating Mean Time To Event For Randomly Left Censored Data, Farzana Chowdhury, Jahida Gulshan, Syed Shahadat Hossain

Journal of Modern Applied Statistical Methods

The aim of this study was to make a comparison among existing estimation methods (Kaplan-Meier, Nelson-Aalen and Regression on Ordered Statistics (ROS)) for randomly left censored time to event data under selected distributions and for different level of censoring and sample sizes in order to determine the strength of these methods based on simulated data. Comparisons among the methods are made on the basis of unbiasedness and Monte Carlo Standard Error of the summary statistics (mean time to event) obtained by those methods under different conditions.