Optimal Design For A Causal Structure, 2019 University of Nebraska-Lincoln
Optimal Design For A Causal Structure, Zaher Kmail
Dissertations and Theses in Statistics
Linear models and mixed models are important statistical tools. But in many natural phenomena, there is more than one endogenous variable involved and these variables are related in a sophisticated way. Structural Equation Modeling (SEM) is often used to model the complex relationships between the endogenous and exogenous variables. It was first implemented in research to estimate the strength and direction of direct and indirect effects among variables and to measure the relative magnitude of each causal factor.
Historically, traditional optimal design theory focuses on univariate linear, nonlinear, and mixed models. There is no current literature on the subject of ...
Effect Of Cross-Validation On The Output Of Multiple Testing Procedures, 2019 University of Arkansas, Fayetteville
Effect Of Cross-Validation On The Output Of Multiple Testing Procedures, Josh Dallas Price
Theses and Dissertations
High dimensional data with sparsity is routinely observed in many scientific disciplines. Filtering out the signals embedded in noise is a canonical problem in such situations requiring multiple testing. The Benjamini--Hochberg procedure using False Discovery Rate control is the gold standard in large scale multiple testing. In Majumder et al. (2009) an internally cross-validated form of the procedure is used to avoid a costly replicate study and the complications that arise from population selection in such studies (i.e. extraneous variables). I implement this procedure and run extensive simulation studies under increasing levels of dependence among parameters and different data ...
Tuning Hyperparameters In Supervised Learning Models And Applications Of Statistical Learning In Genome-Wide Association Studies With Emphasis On Heritability, Jill F. Lundell
All Graduate Theses and Dissertations
Machine learning is a buzz word that has inundated popular culture in the last few years. This is a term for a computer method that can automatically learn and improve from data instead of being explicitly programmed at every step. Investigations regarding the best way to create and use these methods are prevalent in research. Machine learning models can be difficult to create because models need to be tuned. This dissertation explores the characteristics of tuning three popular machine learning models and finds a way to automatically select a set of tuning parameters. This information was used to create an ...
Small Rna Discovery In The Interaction Between Barley And The Powdery Mildew Pathogen, 2019 Iowa State University
Small Rna Discovery In The Interaction Between Barley And The Powdery Mildew Pathogen, Matt Hunt, Sagnik Banerjee, Priyanka Surana, Meiling Liu, Greg Fuerst, Sandra Mathioni, Blake C. Meyers, Dan Nettleton, Roger P. Wise
Background: Plants encounter pathogenic and non-pathogenic microorganisms on a nearly constant basis. Small RNAs such as siRNAs and miRNAs/milRNAs influence pathogen virulence and host defense responses. We exploited the biotrophic interaction between the powdery mildew fungus, Blumeria graminis f. sp. hordei (Bgh), and its diploid host plant, barley (Hordeum vulgare) to explore fungal and plant sRNAs expressed during Bgh infection of barley leaf epidermal cells.
Results: RNA was isolated from four fast-neutron immune-signaling mutants and their progenitor over a time course representing key stages of Bgh infection, including appressorium formation, penetration of epidermal cells, and development of haustorial feeding ...
Classification With The Matrix-Variate-T Distribution, 2019 Iowa State University
Classification With The Matrix-Variate-T Distribution, Geoffrey Z. Thompson, Ranjan Maitra, William Q. Meeker, Ashraf Bastawros
Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-variate t-distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or the classification of functional Magnetic Resonance, satellite or hand gestures images.
Development Of A 1-Dimensional Data Assimilation To Determine Temperature And Relative Humidity Combining Raman Lidar Backscatter Measurements And A Reanalysis Model, 2019 The University of Western Ontario
Development Of A 1-Dimensional Data Assimilation To Determine Temperature And Relative Humidity Combining Raman Lidar Backscatter Measurements And A Reanalysis Model, Shayamila N. Mahagammulla Gamage
Electronic Thesis and Dissertation Repository
Water vapor is the most dominant greenhouse gas in Earth's atmosphere. It is highly variable and its variations strongly depend on changes in temperature. Atmospheric water vapor can be expressed as relative humidity (RH), the ratio of the partial pressure of water vapor in the mixture to the equilibrium vapor pressure of water over a flat surface of pure water at a given temperature. Liquid water can exist as super-cooled water for temperatures between 0C to -38C. Thus, RH can be measured either relative to water (RHw) or to ice (RHi). RHi measurements are important in the upper tropospheric ...
Mathematics Versus Statistics, 2019 Valparaiso University
Mathematics Versus Statistics, Mindy B. Capaldi
Journal of Humanistic Mathematics
Mathematics and statistics are both important and useful subjects, but the former has maintained prominence in the American education system. On the other hand, statistics is more prevalent in daily life and is an increasingly marketable subject to know. This article gives a personal history of one mathematician’s bumpy road to learning and teaching statistics. Additionally, arguments for how and why to include statistics in the K-12 and college curricula are provided.
Choose Your Own Adventure: An Analysis Of Interactive Gamebooks Using Graph Theory, 2019 Southwestern University
Choose Your Own Adventure: An Analysis Of Interactive Gamebooks Using Graph Theory, D'Andre Adams, Daniela Beckelhymer, Alison Marr
Journal of Humanistic Mathematics
"BEWARE and WARNING! This book is different from other books. You and YOU ALONE are in charge of what happens in this story." This is the captivating introduction to every book in the interactive novel series, Choose Your Own Adventure (CYOA). Our project uses the mathematical field of graph theory to analyze forty books from the CYOA book series for ages 9-12. We first began by drawing the digraphs of each book. Then we analyzed these digraphs by collecting structural data such as longest path length (i.e. longest story length) and number of vertices with outdegree zero (i.e ...
Heat Stress Alters Ovarian Insulin-Mediated Phosphatidylinositol-3 Kinase And Steroidogenic Signaling In Gilt Ovaries, Jackson Nteeba, M. Victoria Sanz-Fernandez, Robert P. Rhoads, Lance H. Baumgard, Jason W. Ross, Aileen F. Keating
Heat Stress (HS) compromises a variety of reproductive functions in several mammalian species. Inexplicably, HS animals are frequently hyperinsulinemic despite marked hyperthermia-induced hypophagia. Our objectives were to determine the effects of HS on insulin signaling and components essential to steroid biosynthesis in the pig ovary. Female pigs (35±4 kg) were exposed to constant thermal neutral (TN; 20°C; 35-50% humidity; n = 6) or HS conditions (35°C; 20-35% humidity; n = 6) for either 7 (n = 10) or 35 d (n = 12). After 7d, HS increased (P < 0.05) ovarian mRNA abundance of the insulin receptor (INSR), insulin receptor substrate 1 (IRS1), protein kinase B subunit 1 (AKT1), low density lipoprotein receptor (LDLR), luteinizing hormone receptor (LHCGR), and aromatase (CYP19a). After 35d, HS increased INSR, IRS1, AKT1, LDLR, LHCGR, CYP19a, and steroidogenic acute regulatory protein (STAR) ovarian mRNA abundance. In addition, after 35d, HS increased ovarian phosphorylated IRS1 (pIRS1), phosphorylated AKT (pAKT), STAR and CYP19a protein abundance. Immunostaining analysis revealed similar localization of INSR and pAKT1 in the cytoplasmic membrane and oocyte cytoplasm, respectively, of all stage follicles, and in theca and granulosa cells. Collectively, these results demonstrate that HS alters ovarian insulin mediated-PI3K signaling pathway members which likely impacts follicle activation and viability. In summary, environmentally-induced HS is an endocrine disrupting exposure that modifies ovarian physiology and potentially compromises production of ovarian hormones essential for fertility and pregnancy maintenance.
Adjusting For Spatial Effects In Genomic Prediction, 2019 Fudan University
Adjusting For Spatial Effects In Genomic Prediction, Xiaojun Mao, Somak Dutta, Raymond K. W. Wong, Dan Nettleton
This paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genome-wide association studies (GWAS), spatial effects often affect phenotypic measurements of plants. We consider a Gaussian random field (GRF) model with an additive covariance structure that incorporates genotype effects, spatial effects and subpopulation effects. An empirical study shows the existence of spatial effects and heterogeneity across different subpopulation families while simulations illustrate the improvement in selecting genotypically superior plants by adjusting for spatial effects in genomic prediction.
One And Two-Step Estimation Of Time Variant Parameters And Nonparametric Quantiles, 2019 Kennesaw State University
One And Two-Step Estimation Of Time Variant Parameters And Nonparametric Quantiles, Bogdan Gadidov
Analytics and Data Science Dissertations
This dissertation develops and discusses several one-step and two-step smoothing methods of time variant nonparametric quantiles and time variant parameters from probability models. First, we investigate and develop nonparametric techniques for measuring extreme quantiles. The method involves aggregating data by an explanatory variable such as time and smoothing the resulting data with a nonparametric method like kernel, local polynomial or spline smoothing. We demonstrate both in application and simulation that this two-step procedure of quantile estimation is superior to the parametric quantile regression. We then develop a one-step method which combines the strength of maximum likelihood estimation with a local ...
Some Recent Developments On Pareto-Optimal Reinsurance, 2019 The University of Western Ontario
Some Recent Developments On Pareto-Optimal Reinsurance, Wenjun Jiang
Electronic Thesis and Dissertation Repository
This thesis focuses on developing Pareto-optimal reinsurance policy which considers the interests of both the insurer and the reinsurer. The optimal insurance/reinsurance design has been extensively studied in actuarial science literature, while in early years most studies were concentrated on optimizing the insurer’s interests. However, as early as 1960s, Borch argued that “an agreement which is quite attractive to one party may not be acceptable to its counterparty” and he pioneered the study on “fair” risk sharing between the insurer and the reinsurer. Quite recently, the question of how to strike a balance in risk sharing between an ...
Case-Specific Random Forests For Big Data Prediction, 2019 Iowa State University
Case-Specific Random Forests For Big Data Prediction, Joshua Zimmerman, Dan Nettleton
Some training datasets may be too large for storage on a single computer. Such datasets may be partitioned and stored on separate computers connected in a parallel computing environment. To predict the response associated with a specific target case when training data are partitioned, we propose a method for finding the training cases within each partition that are most relevant for predicting the response of a target case of interest. These most relevant training cases from each partition can be combined into a single dataset, which can be a subset of the entire training dataset that is small enough for ...
Transcript-Based Cloning Of Rrp46, A Regulator Of Rrna Processing And R Gene–Independent Cell Death In Barley–Powdery Mildew Interactions, Liu Xi, Matthew J. Moscou, Yan Meng, Weihui Xu, Rico A. Caldo, Miranda Shaver, Dan Nettleton, Roger P. Wise
Programmed cell death (PCD) plays a pivotal role in plant development and defense. To investigate the interaction between PCD and R gene–mediated defense, we used the 22K Barley1 GeneChip to compare and contrast time-course expression profiles of Blumeria graminis f. sp hordei (Bgh) challenged barley (Hordeum vulgare) cultivar C.I. 16151 (harboring the Mla6 powdery mildew resistance allele) and its fast neutron–derived Bgh-induced tip cell death1 mutant, bcd1. Mixed linear model analysis identified genes associated with the cell death phenotype as opposed to Rgene–mediated resistance. One-hundred fifty genes were found at the threshold P value < 0.0001 and a false discovery rate bcd1mutant. Gene Ontology and rice (Oryza ...
Hierarchical Modeling And Differential Expression Analysis For Rna-Seq Experiments With Inbred And Hybrid Genotypes, Andrew Lithio, Dan Nettleton
The performance of inbred and hybrid genotypes is of interest in plant breeding and genetics. High-throughput sequencing of RNA (RNA-seq) has proven to be a useful tool in the study of the molecular genetic responses of inbreds and hybrids to environmental stresses. Commonly used experimental designs and sequencing methods lead to complex data structures that require careful attention in data analysis. We demonstrate an analysis of RNA-seq data from a split-plot design involving drought stress applied to two inbred genotypes and two hybrids formed by crosses between the inbreds. Our generalized linear modeling strategy incorporates random effects for whole-plot experimental ...
Nested Hierarchical Functional Data Modeling And Inference For The Analysis Of Functional Plant Phenotypes, 2019 University of Nebraska - Lincoln
Nested Hierarchical Functional Data Modeling And Inference For The Analysis Of Functional Plant Phenotypes, Yuhang Xu, Yehua Li, Dan Nettleton
In a plant science Root Image Study, the process of seedling roots bending in response to gravity is recorded using digital cameras, and the bending rates are modeled as functional plant phenotype data. The functional phenotypes are collected from seeds representing a large variety of genotypes and have a three-level nested hierarchical structure, with seeds nested in groups nested in genotypes. The seeds are imaged on different days of the lunar cycle, and an important scientific question is whether there are lunar effects on root bending. We allow the mean function of the bending rate to depend on the lunar ...
Root Type-Specific Reprogramming Of Maize Pericycle Transcriptomes By Local High Nitrate Results In Disparate Lateral Root Branching Patterns, 2019 China Agricultural University
Root Type-Specific Reprogramming Of Maize Pericycle Transcriptomes By Local High Nitrate Results In Disparate Lateral Root Branching Patterns, Peng Yu, Jutta A. Baldauf, Andrew Lithio, Caroline Marcon, Dan Nettleton, Chunjian Li, Frank Hochholdinger
The adaptability of root system architecture to unevenly distributed mineral nutrients in soil is a key determinant of plant performance. The molecular mechanisms underlying nitrate dependent plasticity of lateral root branching across the different root types of maize are only poorly understood. In this study, detailed morphological and anatomical analyses together with cell type-specific transcriptome profiling experiments combining laser capture microdissection with RNA-seq were performed to unravel the molecular signatures of lateral root formation in primary, seminal, crown, and brace roots of maize (Zea mays) upon local high nitrate stimulation. The four maize root types displayed divergent branching patterns of ...
Using Random Forests To Estimate Win Probability Before Each Play Of An Nfl Game, 2019 Iowa State University
Using Random Forests To Estimate Win Probability Before Each Play Of An Nfl Game, Dennis Lock, Dan Nettleton
Before any play of a National Football League (NFL) game, the probability that a given team will win depends on many situational variables (such as time remaining, yards to go for a first down, field position and current score) as well as the relative quality of the two teams as quantified by the Las Vegas point spread. We use a random forest method to combine pre-play variables to estimate Win Probability (WP) before any play of an NFL game. When a subset of NFL play-by-play data for the 12 seasons from 2001 to 2012 is used as a training dataset ...
Stability Of Single-Parent Gene Expression Complementation In Maize Hybrids Upon Water Deficit Stress, Caroline Marcon, Anja Paschold, Waqas Ahmed Malik, Andrew Lithio, Jutta A. Baldauf, Lena Altrogge, Nina Opitz, Christa Lanz, Heiko Schoof, Dan Nettleton, Hans-Peter Piepho, Frank Hochholdinger
Heterosis is the superior performance of F1 hybrids compared with their homozygous, genetically distinct parents. In this study, we monitored the transcriptomic divergence of the maize (Zea mays) inbred lines B73 and Mo17 and their reciprocal F1 hybrid progeny in primary roots under control and water deficit conditions simulated by polyethylene glycol treatment. Single-parent expression (SPE) of genes is an extreme instance of gene expression complementation, in which genes are active in only one of two parents but are expressed in both reciprocal hybrids. In this study, 1,997 genes only expressed in B73 and 2,024 genes only expressed ...
Genomic Neighborhoods For Arabidopsisretrotransposons: A Role For Targeted Integration In The Distribution Of The Metaviridae, 2019 U.S. Department of Agriculture
Genomic Neighborhoods For Arabidopsisretrotransposons: A Role For Targeted Integration In The Distribution Of The Metaviridae, Brooke D. Peterson-Burch, Dan Nettleton, Daniel F. Voytas
Background: Retrotransposons are an abundant component of eukaryotic genomes. The high quality of the Arabidopsis thaliana genome sequence makes it possible to comprehensively characterize retroelement populations and explore factors that contribute to their genomic distribution.
Results: We identified the full complement of A. thaliana long terminal repeat (LTR) retroelements using RetroMap, a software tool that iteratively searches genome sequences for reverse transcriptases and then defines retroelement insertions. Relative ages of full-length elements were estimated by assessing sequence divergence between LTRs: the Pseudoviridae were significantly younger than the Metaviridae. All retroelement insertions were mapped onto the genome sequence and their distribution ...