Other Statistics and Probability Commons^™

Genomic-Enabled Prediction Of Ordinal Data With Bayesian Logistic Ordinal Regression, Osval A. Montesinos-López, Abelardo Montesinos-López, José Crossa, Juan Burgueño, Kent M. Eskridge

Department of Statistics: Faculty Publications

Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPORmodel …

Establishment And Persistence Of Yellow-Flowered Alfalfa No-Till Interseeded Into Crested Wheatgrass Stands, Christopher G. Misar, Lan Xu, Roger N. Gates, Arvid Boe, Patricia S. Johnson, Christopher S. Schauer, John R. Rickertsen, Walter Stroup

Department of Statistics: Faculty Publications

Crested wheatgrass [Agropyron cristatum (L.) Gaertn., A. desertorum

(Fisch. ex Link) Schult., and related taxa] often exists

in near monoculture stands in the northern Great Plains.

Introducing locally adapted yellow-flowered alfalfa [Medicago

sativa L. subsp. falcata (L.) Arcang.] would complement crested

wheatgrass. Our objective was to evaluate effects of seeding

date, clethodim {(E) -2-[1-[[(3-chloro-2-propenyl)oxy]imino]

propyl]-5-[2-(ethylthio)propyl]-3-hydroxy-2-cyclohexen-1-one}

sod suppression, and seeding rate on initial establishment and

stand persistence of Falcata, a predominantly yellow-flowered

alfalfa, no-till interseeded into crested wheatgrass. Research was

initiated in August 2008 at Newcastle, WY; Hettinger, ND;

Fruitdale, SD; and Buffalo, SD. Effects of treatment …

Effect Of Dexamethasone Prodrug On Inflamed Temporomandibular Joints In Juvenile Rats, Mitchell Knudsen, Matthew Bury, Callie Holwegner, Adam L. Reinhardt, Fang Yuan, Yijia Zhang, Peter Giannini, D. B. Marx, Dong Wang, Richard A. Reinhardt

Department of Statistics: Faculty Publications

Introduction: Juvenile idiopathic arthritis (JIA) often causes inflammation of the temporomandibular joint (TMJ) and has been treated with both systemic and intra-articular steroids, with concerns about effects on growing bones. In this study, we evaluated the impact of a macromolecular prodrug of dexamethasone (P-DEX) with inflammation-targeting potential applied systemically or directly to the TMJ.

Methods: Joint inflammation was initiated by injecting two doses of complete Freund’s adjuvant (CFA) at 1-month intervals into the right TMJs of 24 growing Sprague–Dawley male rats (controls on left side). Four additional rats were not manipulated. With the second CFA injection, animals received (1) 5 …

Threshold Models For Genome-Enabled Prediction Of Ordinal Categorical Traits In Plant Breeding, Osval A. Montesinos-López, Abelardo Montesinos-López, Paulino Pérez-Rodríguez, Gustavo De Los Campos, Kent M. Eskridge, José Crossa

Department of Statistics: Faculty Publications

Categorical scores for disease susceptibility or resistance often are recorded in plant breeding. The aim of this study was to introduce genomic models for analyzing ordinal characters and to assess the predictive ability of genomic predictions for ordered categorical phenotypes using a threshold model counterpart of the Genomic Best Linear Unbiased Predictor (i.e., TGBLUP). The threshold model was used to relate a hypothetical underlying scale to the outward categorical response. We present an empirical application where a total of nine models, five without interaction and four with genomic x environment interaction (G·E) and genomic additive x additive x environment interaction …

Measuring Peer Socialization For Adolescent Substance Use:A Comparison Of Perceived And Actual Friends’ Substance Use Effects, Arielle R. Deutsch, Pavel Chernyavskiy, Douglas Steinley, Wendy S. Slutske

Department of Statistics: Faculty Publications

Objective: There has been an increase in the use of social network analysis in studies of peer socialization effects on adolescent substance use. Some researchers argue that social network analyses provide more accurate measures of peer substance use, that the alternate strategy of assessing perceptions of friends’ drug use is biased, and that perceptions of peer use and actual peer use represent different constructs. However, there has been little research directly comparing the two effects, and little is known about the extent to which the measures differ in the magnitude of their influence on adolescent substance use, as well as …

A Copula Based Approach For Design Of Multivariate Random Forests For Drug Sensitivity Prediction, Saad Haider, Raziur Rahman, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships …

Design Of Probabilistic Random Forests With Applications To Anticancer Drug Sensitivity Prediction, Raziur Rahman, Saad Haider, Souparno Gosh, Ranadip Pal

Department of Statistics: Faculty Publications

Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees’ prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. …

S1: Supplementary Information For Article: A Copula Based Approach For Design Of Multivariate Random Forests For Drug Sensitivity Prediction, Saad Haider, Raziur Rahman, Souparno Ghosh, Ranadip Pal

Department of Statistics: Faculty Publications

Changes in performance with prior feature selection

Random forest (RF) is designed to create uncorrelated trees using random subsets of features in each node of each tree. RF by itself is a great tool for feature selection from a high dimensional set of features. But we observed that the prediction accuracy is improved when a prior feature selection (RELIEFF) [1] approach is implemented. Table A shows the performance of RF, VMRF and CMRF with and without RELIEFF feature selection in 2 drug sets of GDSC.

Performance Analysis for drugsets consisting of more 8 than two drugs

We have generated empirical …

Voc Emissions From Beef Feedlot Pen Surfaces As Affected By Within-Pen Location, Moisture And Temperature, Bryan L. Woodbury, John E. Gilley, David B. Parker, David B. Marx, Roger A. Eigenberg

Department of Statistics: Faculty Publications

A laboratory study was conducted to evaluate the effects of pen location, moisture, and temperature on emissions of volatile organic compounds (VOC) from surface materials obtained from feedlot pens where beef cattle were fed a diet containing 30% wet distillers grain plus solubles. Surface materials were collected from the feed trough (bunk), drainage, and raised areas (mounds) within three feedlot pens. The surface materials were mixed with water to represent dry, wet, or saturated conditions and then incubated at temperatures of 5, 15, 25 and 35 C. A wind tunnel and gas chromatograph-mass spectrometer were used to collect and quantify …