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Articles 1 - 10 of 10
Full-Text Articles in Statistical Models
Interval Estimation Of Proportion Of Second-Level Variance In Multi-Level Modeling, Steven Svoboda
Interval Estimation Of Proportion Of Second-Level Variance In Multi-Level Modeling, Steven Svoboda
The Nebraska Educator: A Student-Led Journal
Physical, behavioral and psychological research questions often relate to hierarchical data systems. Examples of hierarchical data systems include repeated measures of students nested within classrooms, nested within schools and employees nested within supervisors, nested within organizations. Applied researchers studying hierarchical data structures should have an estimate of the intraclass correlation coefficient (ICC) for every nested level in their analyses because ignoring even relatively small amounts of interdependence is known to inflate Type I error rate in single-level models. Traditionally, researchers rely upon the ICC as a point estimate of the amount of interdependency in their data. Recent methods utilizing an …
Statistical Methodology To Establish A Benchmark For Evaluating Antimicrobial Resistance Genes Through Real Time Pcr Assay, Enakshy Dutta
Statistical Methodology To Establish A Benchmark For Evaluating Antimicrobial Resistance Genes Through Real Time Pcr Assay, Enakshy Dutta
Department of Statistics: Dissertations, Theses, and Student Work
Novel diagnostic tests are usually compared with gold standard tests for evaluating diagnostic accuracy. For assessing antimicrobial resistance (AMR) to bovine respiratory disease (BRD) pathogens, phenotypic broth microdilution method is used as gold standard (GS). The objective of the thesis is to evaluate the optimal cycle threshold (Ct) generated by real-time polymerase chain reaction (rtPCR) to genes that confer resistance that will translate to the phenotypic classification of AMR. Data from two different methodologies are assessed to identify Ct that will discriminate between resistance (R) and susceptibility (S). First, the receiver operating characteristic (ROC) curve was used to determine the …
Statistical Investigation Of Road And Railway Hazardous Materials Transportation Safety, Amirfarrokh Iranitalab
Statistical Investigation Of Road And Railway Hazardous Materials Transportation Safety, Amirfarrokh Iranitalab
Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research
Transportation of hazardous materials (hazmat) in the United States (U.S.) constituted 22.8% of the total tonnage transported in 2012 with an estimated value of more than 2.3 billion dollars. As such, hazmat transportation is a significant economic activity in the U.S. However, hazmat transportation exposes people and environment to the infrequent but potentially severe consequences of incidents resulting in hazmat release. Trucks and trains carried 63.7% of the hazmat in the U.S. in 2012 and are the major foci of this dissertation. The main research objectives were 1) identification and quantification of the effects of different factors on occurrence and …
Essentials Of Structural Equation Modeling, Mustafa Emre Civelek
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 …
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
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 …
Beta-Binomial Kriging: A New Approach To Modeling Spatially Correlated Proportions, Aimee Schwab
Beta-Binomial Kriging: A New Approach To Modeling Spatially Correlated Proportions, Aimee Schwab
Department of Statistics: Dissertations, Theses, and Student Work
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 …
Best Practice Recommendations For Data Screening, Justin A. Desimone, Peter D. Harms, Alice J. Desimone
Best Practice Recommendations For Data Screening, Justin A. Desimone, Peter D. Harms, Alice J. Desimone
Department of Management: Faculty Publications
Survey respondents differ in their levels of attention and effort when responding to items. There are a number of methods researchers may use to identify respondents who fail to exert sufficient effort in order to increase the rigor of analysis and enhance the trustworthiness of study results. Screening techniques are organized into three general categories, which differ in impact on survey design and potential respondent awareness. Assumptions and considerations regarding appropriate use of screening techniques are discussed along with descriptions of each technique. The utility of each screening technique is a function of survey design and administration. Each technique has …
Significant Themes In 19th-Century Literature, Matthew L. Jockers, David Mimno
Significant Themes In 19th-Century Literature, Matthew L. Jockers, David Mimno
Department of English: Faculty Publications
External factors such as author gender, author nationality, and date of publication affect both the choice of literary themes in novels and the expression of those themes, but the extent of this association is difficult to quantify. In this work, we apply statistical methods to identify and extract hundreds of "topics" from a corpus of 3,346 works of 19th-century British, Irish, and American fiction. We use these topics as a measurable, data-driven proxy for literary themes. External factors may predict fluctuations in the use of themes and the individual word choices within themes. We use topics to measure the evidence …
Sequence Comparison And Stochastic Model Based On Multi-Order Markov Models, Xiang Fang
Sequence Comparison And Stochastic Model Based On Multi-Order Markov Models, Xiang Fang
Department of Statistics: Dissertations, Theses, and Student Work
This dissertation presents two statistical methodologies developed on multi-order Markov models. First, we introduce an alignment-free sequence comparison method, which represents a sequence using a multi-order transition matrix (MTM). The MTM contains information of multi-order dependencies and provides a comprehensive representation of the heterogeneous composition within a sequence. Based on the MTM, a distance measure is developed for pair-wise comparison of sequences. The new method is compared with the traditional maximum likelihood (ML) method, the complete composition vector (CCV) method and the improved version of the complete composition vector (ICCV) method using simulated sequences. We further illustrate the application of …
The Em Algorithm For Group Testing Regression Models Under Matrix Pooling, Christopher R. Bilder, Boan Zhang
The Em Algorithm For Group Testing Regression Models Under Matrix Pooling, Christopher R. Bilder, Boan Zhang
Department of Statistics: Faculty Publications
No abstract provided.