Applied Statistics Commons^™

A Response To Anderson's (2013) Conceptual Distinction Between The Critical P Value And Type I Error Rate In Permutation Testing, Fortunato Pesarin, Stefano Bonnini

Journal of Modern Applied Statistical Methods

Pesarin and Bonnini respond to Anderson's (2013) Conceptual Distinction between the Critical p value and Type I Error Rate in Permutation Testing

A Monte Carlo Simulation Of The Robust Rank-Order Test Under Various Population Symmetry Conditions, William T. Mickelson

Journal of Modern Applied Statistical Methods

The Type I Error Rate of the Robust Rank Order test under various population symmetry conditions is explored through Monte Carlo simulation. Findings indicate the test has difficulty controlling Type I error under generalized Behrens-Fisher conditions for moderately sized samples.

Constructing A More Powerful Test In Two-Level Block Randomized Designs, Spyros Konstantopoulos

Journal of Modern Applied Statistical Methods

A more powerful test is proposed for the treatment effect in two-level block randomized designs where random assignment takes place at the first level. When clustering at the second level is assumed to be known, the proposed test produces higher estimates of power than the typical test.

Bayesian Inference Of Pair-Copula Constriction For Multivariate Dependency Modeling Of Iran’S Macroeconomic Variables, M. R. Zadkarami, O. Chatrabgoun

Journal of Modern Applied Statistical Methods

Bayesian inference of pair-copula constriction (PCC) is used for multivariate dependency modeling of Iran’s macroeconomics variables: oil revenue, economic growth, total consumption and investment. These constructions are based on bivariate t-copulas as building blocks and can model the nature of extreme events in bivariate margins individually. The model parameter was estimated based on Markov chain Monte Carlo (MCMC) methods. A MCMC algorithm reveals unconditional as well as conditional independence in Iran’s macroeconomic variables, which can simplify resulting PCC’s for these data.

Using The Bootstrap For Estimating The Sample Size In Statistical Experiments, Maher Qumsiyeh

Journal of Modern Applied Statistical Methods

Efron’s (1979) Bootstrap has been shown to be an effective method for statistical estimation and testing. It provides better estimates than normal approximations for studentized means, least square estimates and many other statistics of interest. It can be used to select the active factors - factors that have an effect on the response - in experimental designs. This article shows that the bootstrap can be used to determine sample size or the number of runs required to achieve a certain confidence level in statistical experiments.

The X-Alter Algorithm: A Parameter-Free Method Of Unsupervised Clustering, Thomas Laloë, Rémi Servien

Journal of Modern Applied Statistical Methods

Using quantization techniques, Laloë (2010) defined a new clustering algorithm called Alter. This L¹-based algorithm is shown to be convergent but suffers two major flaws. The number of clusters, K, must be supplied by the user and the computational cost is high. This article adapts the X-means algorithm (Pelleg & Moore, 2000) to solve both problems.

Estimating Heterogeneous Intra-Class Correlation Coefficients In Dyadic Ecological Momentary Assessment, Emily A. Blood, Leslie A. Kalish, Lydia A. Shrier

Journal of Modern Applied Statistical Methods

A method is described for estimating and testing predictors for influence on the variance of momentary behaviors in dyadic ecological momentary assessment data. Results show that the method allows intraclass correlations of momentary observations from two members of the same couple to vary by observation-level, individual-level and couple-level predictors.

Jmasm 32: Multiple Imputation Of Missing Multilevel, Longitudinal Data: A Case When Practical Considerations Trump Best Practices?, Jennifer E. V. Lloyd, Jelena Obradović, Richard M. Carpiano, Frosso Motti-Stefanidi

Journal of Modern Applied Statistical Methods

A pedagogical tool is presented for applied researchers dealing with incomplete multilevel, longitudinal data. It explains why such data pose special challenges regarding missingness. Syntax created to perform a multiply-imputed growth modeling procedure in Stata Version 11 (StataCorp, 2009) is also described.

Bootstrap Interval Estimation Of Reliability Via Coefficient Omega, Miguel A. Padilla, Jasmin Divers

Journal of Modern Applied Statistical Methods

Three different bootstrap confidence intervals (CIs) for coefficient omega were investigated. The CIs were assessed through a simulation study with conditions not previously investigated. All methods performed well; however, the normal theory bootstrap (NTB) CI had the best performance because it had more consistent acceptable coverage under the simulation conditions investigated.

Fitting Proportional Odds Models To Educational Data With Complex Sampling Designs In Ordinal Logistic Regression, Xing Liu, Hari Koirala

Journal of Modern Applied Statistical Methods

The conventional proportional odds (PO) model assumes that data are collected using simple random sampling by which each sampling unit has the equal probability of being selected from a population. However, when complex survey sampling designs are used, such as stratified sampling, clustered sampling or unequal selection probabilities, it is inappropriate to conduct ordinal logistic regression analyses without taking sampling design into account. Failing to do so may lead to biased estimates of parameters and incorrect corresponding variances. This study illustrates the use of PO models with complex survey data to predict mathematics proficiency levels using Stata and compare the …

Environmentally Friendly Sizing Agent From Corn Distillers Dried Grains, Yue Zhang

College of Education and Human Sciences: Dissertations, Theses, and Student Research

Distillers dried grains (DDGS), the coproducts of corn ethanol production, were used as a textile sizing agent on cotton, polyester and polyester/cotton blends in an effort to find inexpensive and biodegradable alternatives to sizing agents such as poly(vinyl alcohol) that are currently used. Although DDGS is an inexpensive, biodegradable and abundant co-product, it has limited industrial applications. DDGS is a mixture of carbohydrates, proteins and oil which are used as sizing agents or as size additives. The effects of DDGS extraction conditions on sizing evaluation parameters such as fiber adhesion, film properties, viscosity and fabric abrasion were studied in comparison …

Assessment Of Tillage Practices Using Landsat-Tm 5 In Nebraska., Sonisa Sharma

School of Natural Resources: Dissertations, Theses, and Student Research

Tillage management practices are an important component to crop production and to federal and state conservation efforts and crop subsidy programs. Crop residue created by conservation tillage reduces soil erosion and reduce evaporation from exposed soil. Agro-hydrological models require information on tillage practices to estimate their impacts on soil-water-holding capacity, total evapotranspiration, carbon sequestration, water runoff and water and wind erosion for agricultural lands. Classification of tillage practices using remote sensing offers promise for the rapid collection of tillage information on individual fields over large areas. Using satellite imagery proves to be challenging due to the similarity in spectral signatures …

Professor Salaries At Stephen F. Austin State University, Jami Miller

Undergraduate Research Conference

When I and my group mates started this project, we thought that professor salaries would average $70,000. We also thought that all the following would be significant and positive influences on salary: being male, a full time professor, having a doctorate, and the college taught in.

Customer Age As A Predictor Of Contact Volume, Tollan Renner

Honors Theses and Capstones

A two stage modeling approach for modeling customer age as a predictor of contact volume was conducted using a real-world data set of approximately 2,000,000 contacts from a company call center. Two models were constructed in the first stage, one a straightforward regression and the other a series of regressions. One was selected as better performing and scaled up to predict calls received from calls answered. The second stage of the modeling included a day of the week covariate and performed the best of the models created. This model uses age bins as model effects, of which the youngest age …

Mathematical Modelling And Control Of Echinococcus In Qinghai Province, China, Liumei Wu, Baojun Song, Wen Du, Jie Lou

Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works

In this paper, two mathematical models, the baseline model and the intervention model, are proposed to study the transmission dynamics of echinococcus. A global forward bifurcation completely characterizes the dynamical behavior of the baseline model. That is, when the basic reproductive number is less than one, the disease-free equilibrium is asymptotically globally stable; when the number is greater than one, the endemic equilibrium is asymptotically globally stable. For the intervention model, however, the basic reproduction number alone is not enough to describe the dynamics, particularly for the case where the basic reproductive number is less then one. The emergence of …

Analysis Of Continuous Longitudinal Data With Arma(1, 1) And Antedependence Correlation Structures, Sirisha Mushti

Mathematics & Statistics Theses & Dissertations

Longitudinal or repeated measure data are common in biomedical and clinical trials. These data are often collected on individuals at scheduled times resulting in dependent responses. Inference methods for studying the behavior of responses over time as well as methods to study the association with certain risk factors or covariates taking into account the dependencies are of great importance. In this research we focus our study on the analysis of continuous longitudinal data. To model the dependencies of the responses over time, we consider appropriate correlation structures generated by the stationary and non-stationary time-series models. We develop new estimation procedures …

A Bayesian Regression Tree Approach To Identify The Effect Of Nanoparticles Properties On Toxicity Profiles, Cecile Low-Kam, Haiyuan Zhang, Zhaoxia Ji, Tian Xia, Jeffrey I. Zinc, Andre Nel, Donatello Telesca

COBRA Preprint Series

We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose and time-response surfaces smoothing. The resulting posterior distribution is sampled via a Markov Chain Monte Carlo algorithm. This …

New Tools For Quantitative Decision Analysis In Applied Ecology And Conservation, Adam W. Schapaugh

School of Natural Resources: Dissertations, Theses, and Student Research

Scientists have generated a massive body of theory aimed at predicting and managing the impacts of anthropogenic activities on populations, species, and ecosystems. Transforming this research into knowledge that informs complex decision-making problems remains a major challenge in environmental management and conservation. My dissertation research aims to address this issue through the development and application of mathematical and statistical models. I integrate tools, concepts, and techniques from ecology, applied mathematics, computer science, and statistics to build structured decision-making frameworks for spatial prioritization, resource allocation, and optimal scheduling. I also tackle several of the technical challenges limiting the utility of such …

Missing At Random And Ignorability For Inferences About Subsets Of Parameters With Missing Data, Roderick J. Little, Sahar Zanganeh

The University of Michigan Department of Biostatistics Working Paper Series

For likelihood-based inferences from data with missing values, Rubin (1976) showed that the missing data mechanism can be ignored when (a) the missing data are missing at random (MAR), in the sense that missingness does not depend on the missing values after conditioning on the observed data, and (b) the parameters of the data model and the missing-data mechanism are distinct; that is, there are no a priori ties, via parameter space restrictions or prior distributions, between the parameters of the data model and the parameters of the model for the mechanism. Rubin described (a) and (b) as the "weakest …

Some Minor-Closed Classes Of Signed Graphs, Dan Slilaty, Xiangqian Zhou

Mathematics and Statistics Faculty Publications

We define four minor-closed classes of signed graphs in terms of embeddability in the annulus, projective plane, torus, and Klein bottle. We give the full list of 20 excluded minors for the smallest class and make a conjecture about the largest class.

Synthesis: What We Have Learned From The East Texas Radiocarbon Database, Robert Z. Selden Jr., Timothy K. Perttula

CRHR: Archaeology

This poster provides a short overview of what we have learned from the East Texas Radiocarbon Database since it became available on the Council of Texas Archeologists’ website in 2011. These successes are numerous and include the advancement of novel methodological approaches; an improvement in our comprehension of the temporal nuances within the East Texas Archaic; the division of the East Texas Woodland period into Early, Middle and Late; the refinement of Caddo temporal chronology – particularly from a geographic perspective -- and it has provided one line of evidence to use to argue for the fluorescence of corn-based agriculture …

Ceramic Petrofacies: Modeling The Angelina River Basin In East Texas, Robert Z. Selden Jr.

CRHR: Archaeology

Ceramic provenance studies remain the basis of worldwide archaeological research concerned with reconstructing exchange networks, tracing migrations, and informing upon ceramic economy. Unfortunately, Texas archaeologists have been plagued with an inability to trace ceramic production sources to the same extent as researchers within other regions. Ceramic petrofacies models have been employed successfully in archaeological contexts at the San Pedro Valley, Tonto basin, Tucson basin, Agua Fria, and Gila and Phoenix basins in Arizona, but have not yet been employed east of Arizona. Data resulting from the construction of an actualistic petrofacies model in the prehistoric coastal environment of East Texas …

Epistemology And Synthesis: Instrumental Neutron Activation Analysis And The Caddo Tradition, Robert Z. Selden Jr.

CRHR: Archaeology

The statistical groupings illustrated herein represent the current iteration of Caddo INAA compositional groups based upon the chemical composition of archaeologically-recovered ceramics. For some time, a number of Caddo archaeologists have thought these results to be lacking. This poster symbolizes the first step toward a new interpretation of chemical composition groups, and the initial instancce within which GIS has been employed as an analytical tool.

Spatial Dynamics Of U.S. Cultural Resource Law, Robert Z. Selden Jr., C. Britt Bousman

CRHR: Archaeology

The American Antiquities Act, Historic Sites Act, Archeological and Historic Preservation Act, National Historic Preservation Act, American Indian Religious Freedom Act, Archeological Resources Protection Act, Abandoned Shipwreck Act, and the Native American Graves Protection and Repatriation Act comprise the basis of our exploration of cultural resource legislation in the United States. Since the passage of the American Antiquities Act in 1906, 1086 cases have challenged these statutes in U.S. courts. We investigate temporal and regional patterns of the case law to establish whether these laws are uniformly prosecuted throughout the U.S. Our findings suggest that case law is complex and …

Radiocarbon Trends And The East Texas Caddo Tradition (Ca. A.D. 800-1680), Robert Z. Selden Jr., Timothy K. Perttula

CRHR: Archaeology

Through the employment of radiocarbon (¹⁴C) dates as data, we use the date combination process to refine site-specific summed probability distributions for 555 dates from Caddo sites (n = 19) in East Texas with 10 or more ¹⁴C dates. Summed probability distributions are then contrasted across river basins and natural regions with the remainder of the East Texas Caddo Radiocarbon Database (n = 338 dates from 132 other Caddo sites), highlighting the temporal and spatial character of Caddo archaeological sites throughout East Texas.

Investigation Of A Pregnancy Lifestyle Intervention Using Mediation Analysis And A Power Analysis Simulation, Kelsey Grantham

Statistics

No abstract provided.

The Psychological Impacts Of False Positive Ovarian Cancer Screening: Assessment Via Mixed And Trajectory Modeling, Amanda T. Wiggins

Theses and Dissertations--Epidemiology and Biostatistics

Ovarian cancer (OC) is the fifth most common cancer among women and has the highest mortality of any cancer of the female reproductive system. The majority (61%) of OC cases are diagnosed at a distant stage. Because diagnoses occur most commonly at a late-stage and prognosis for advanced disease is poor, research focusing on the development of effective OC screening methods to facilitate early detection in high-risk, asymptomatic women is fundamental in reducing OC-specific mortality. Presently, there is no screening modality proven efficacious in reducing OC-mortality. However, transvaginal ultrasonography (TVS) has shown value in early detection of OC. TVS presents …

New Microarray Image Segmentation Using Segmentation Based Contours Method, Yuan Cheng

Doctoral Dissertations

The goal of the research developed in this dissertation is to develop a more accurate segmentation method for Affymetrix microarray images. The Affymetrix microarray biotechnologies have become increasingly important in the biomedical research field. Affymetrix microarray images are widely used in disease diagnostics and disease control. They are capable of monitoring the expression levels of thousands of genes simultaneously. Hence, scientists can get a deep understanding on genomic regulation, interaction and expression by using such tools.

We also introduce a novel Affymetrix microarray image simulation model and how the Affymetrix microarray image is simulated by using this model. This simulation …

Nfl Betting Market: Using Adjusted Statistics To Test Market Efficiency And Build A Betting Model, James P. Donnelly

CMC Senior Theses

The use of statistical analysis has been prevalent in the sports gambling industry for years. More recently, we have seen the emergence of "adjusted statistics", a more sophisticated way to examine each play and each result (further explanation below). And while adjusted statistics have become commonplace for professional and recreational bettors alike, little research has been done to justify their use. In this paper the effectiveness of this data is tested on the most heavily wagered sport in the world – the National Football League (NFL). The results are studied with two central questions in mind: Does the market account …

Connecting Big Data With Big Decisions: Ideas For Synthesizing Analytics And Decision Analysis, Jeffrey Keisler

Management Science and Information Systems Faculty Publication Series

This paper describes an approach to connect decision analysis models with outputs of analytic methods applied to various types of big data. Decision analysis models focus on issues of concern to a decision maker and incorporate use of a range of methods and axioms to develop insights about what the decision maker should do. In particular, decision analysis models typically use subjective judgments from the decision maker to describe beliefs about the likelihood of events and the desirability of outcomes. In order for human judgments to be improved by the availability of large amounts of data and processing power, it …