Other Statistics and Probability Commons^™

Practical Guidelines For The Comprehensive Analysis Of Chip-Seq Data, Timonthy Bailey, Pawel Krajewski, Istvan Ladunga, Celine Lefebvre, Qunhua Li, Tao Liu, Pedro Madrigal, Cenny Taslim, Jie Zhang

Department of Statistics: Faculty Publications

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality …

Stochastic Simulation And Spatial Statistics Of Large Datasets Using Parallel Computing, Jonathan Sw Lee

Electronic Thesis and Dissertation Repository

Lattice models are a way of representing spatial locations in a grid where each cell is in a certain state and evolves according to transition rules and rates dependent on a surrounding neighbourhood. These models are capable of describing many phenomena such as the simulation and growth of a forest fire front. These spatial simulation models as well as spatial descriptive statistics such as Ripley's K-function have wide applicability in spatial statistics but in general do not scale well for large datasets. Parallel computing (high performance computing) is one solution that can provide limited scalability to these applications. This is …

Research On The Establishment Of Promulgation System Of Maritime Safety Information In Chengshan Jiao Vts Center, Yunjiang Liu

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose

Doctoral Dissertations

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. …

Smart Sampling Of Noble Gases To Detect Underground Nuclear Explosions, Lindsey M. Skelton, Steven Hunter, Charles Carrigan

STAR Program Research Presentations

One element of the Comprehensive Nuclear Test Ban Treaty (CTBT) is the provision for an on site inspection (OSI). The purpose of an OSI is to monitor for the occurrence of an underground nuclear explosion (UNE) in violation of the treaty. Detection of certain rare radioactive noble gases transported to the surface can be an excellent indicator of a UNE. These gases can be very difficult to capture and require specialized sampling methods. This study aims to determine an algorithm that will increase the efficiency of the subsurface gas sampling technique being used to detect UNEs. Continuous sampling of subsurface …

Verifying R Code And Visualizing Power Grid Data: Signature Quality Metrics And Gui Development, Emmanuel C. Herrera, Brett Amidan

STAR Program Research Presentations

Verifying R code and visualizing Power Grid data:

Signature Quality Metrics and GUI development

Author: Emmanuel Herrera

Mentors: Brett Amidan and Landon Sego

There are many bioforensic signatures produced by analytical instruments that are expensive to produce and maintain accuracy. SQM is an R package which will provide subject matter experts with tools that will help them assess the specific quality of signatures and determine their accuracy, utility and cost by simple function calls. Many published academic papers were surveyed on the kinds of metrics already being implemented. Once understood how some metrics measured accuracy, the SQM package was duplicated …

Level Crossing Times In Mathematical Finance, Ofosuhene Osei

Electronic Theses and Dissertations

Level crossing times and their applications in finance are of importance, given certain threshold levels that represent the "desirable" or "sell" values of a stock. In this thesis, we make use of Wald's lemmas and various deep results from renewal theory, in the context of finance, in modelling the growth of a portfolio of stocks. Several models are employed .

Comparison Of Option Pricing Between Arma-Garch And Garch-M Models, Yi Xi

Electronic Thesis and Dissertation Repository

Option pricing is a major area in financial modeling. Option pricing is sometimes based on normal GARCH models. Normal GARCH models fail to capture the skewness and the leptokurtosis in financial data. The variant GARCH-in-mean (GARCH-M) model is widely used in the option pricing literature. It adds a heteroskedasticity term to the mean equation, which is interpreted as a risk premium, and also incorporates a type of asymmetry.

Our goal is to compare option valuation between GARCH-M and ARMA-GARCH models with normal and non-normal, z-distributed innovations. The models are fitted to the historical return data, and risk neutral measures are …

Correlation Coefficient Of Interval Neutrosophic Set, Said Broumi, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this paper we introduce for the first time the concept of correlation coefficients of interval valued neutrosophic set (INS for short). Respective numerical examples are presented.

Drosophila Melanogaster Selection For Survival After Infection With Bacillus Cereus Spores: Evolutionary Genetic And Phenotypic Investigations Of Respiration And Movement, Junjie Ma, Andrew K. Benson, Stephen D. Kachman, Deidra J. Jacobsen, Lawrence G. Harshman

Department of Statistics: Faculty Publications

Laboratory populations of D.melanogaster have been subjected to selection for survival after live spores of B. cereus were introduced as a pathogenic agent. The present study was designed to investigate correlated traits: respiration as ametabolic trait and movement as a behavioral trait.An underlying hypothesiswas that the evolution of increased survival after B. cereus infection exerts ametabolic cost associated with elevated immunity and this would be detected by increased respiration rates. There was support for this hypothesis in the male response to selection, but not for selected-line females. Two phenotypic effects were also observed in the study. Females especially showed a …

The Change In Differing Leukocyte Populations During Vaccination To Bovine Respiratory Disease And Their Correlations With Lung Scores, Health Records, And Average Daily Gain, R. J. Leach, C. G. Chitko-Mckown, G. L. Bennett, S. A. Jones, Stephen D. Kachman, J. W. Keele, K. A. Leymaster, R. M. Thallman, L. A. Kuehn

Department of Statistics: Faculty Publications

Bovine respiratory disease (BRD) is the most economically important disease in U.S. feedlots. Infection can result in morbidity, mortality, and reduced average daily gain. Cheap and reliable genetic methods of prediction and protection from BRD would be highly advantageous to the industry. The immune response may correlate with BRD incidence. Cattle (n = 2,182) were vaccinated against common viral and bacterial pathogens of BRD. Two blood samples were collected, one during booster vaccination and one 21d later, enabling 3 phenotypes for each trait [prebooster (pre), postbooster (post), and delta (post minus pre)]. From the blood samples innate and adaptive responses …

Prediction In M-Complete Problems With Limited Sample Size, Jennifer Lynn Clarke, Bertrand Clarke, Chi-Wai Yu

Department of Statistics: Faculty Publications

We define a new Bayesian predictor called the posterior weighted median (PWM) and compare its performance to several other predictors including the Bayes model average under squared error loss, the Barbieri-Berger median model predictor, the stacking predictor, and the model average predictor based on Akaike's information criterion. We argue that PWM generally gives better performance than other predictors over a range of M-complete problems. This range is between the M-closed-M-complete boundary and the M-complete- M-open boundary. Indeed, as a problem gets closer to M-open, it seems that M-complete predictive methods begin to break down. Our comparisons rest on extensive simulations …

Comparison Of Molecular Breeding Values Based On Within- And Across-Breed Training In Beef Cattle, Stephen D. Kachman, Matthew L. Spangler, Gary L. Bennett, Kathryn J. Hanford, Larry A. Kuehn, Warren M. Snelling, Mark Thallman, Mahdi Saatchi, Dorian J. Garrick, Robert D. Schnabel, Jeremy F. Taylor, E John Pollak

Department of Statistics: Faculty Publications

Background: Although the efficacy of genomic predictors based on within-breed training looks promising, it is necessary to develop and evaluate across-breed predictors for the technology to be fully applied in the beef industry. The efficacies of genomic predictors trained in one breed and utilized to predict genetic merit in differing breeds based on simulation studies have been reported, as have the efficacies of predictors trained using data from multiple breeds to predict the genetic merit of purebreds. However, comparable studies using beef cattle field data have not been reported.

Methods: Molecular breeding values for weaning and yearling weight …

Mapping And Decomposing Scale-Dependent Soil Moisture Variability Within An Inner Bluegrass Landscape, Carla Landrum

Theses and Dissertations--Plant and Soil Sciences

There is a shared desire among public and private sectors to make more reliable predictions, accurate mapping, and appropriate scaling of soil moisture and associated parameters across landscapes. A discrepancy often exists between the scale at which soil hydrologic properties are measured and the scale at which they are modeled for management purposes. Moreover, little is known about the relative importance of hydrologic modeling parameters as soil moisture fluctuates with time. More research is needed to establish which observation scales in space and time are optimal for managing soil moisture variation over large spatial extents and how these scales are …

Do Non-Response Follow-Ups Improve Or Reduce Data Quality?: A Review Of The Existing Literature, Kristen Olson

Department of Sociology: Faculty Publications

The paper systematically reviews existing literature on the relationship between the level of effort to recruit a sampled person and the measurement quality of survey data. Hypotheses proposed for this relationship are reviewed. Empirical findings for the relationship between level of effort as measured by paradata (the number of follow-up attempts, refusal conversion and time in the field) and question-specific item non-response rates, aggregate measures of item non-response rates, response accuracy and various measurement errors on attitudinal questions are examined through a qualitative review.

A Comparison Of Periodic Autoregressive And Dynamic Factor Models In Intraday Energy Demand Forecasting, Thomas Mestekemper, Goeran Kauermann, Michael Smith

Michael Stanley Smith

We suggest a new approach for forecasting energy demand at an intraday resolution. Demand in each intraday period is modeled using semiparametric regression smoothing to account for calendar and weather components. Residual serial dependence is captured by one of two multivariate stationary time series models, with dimension equal to the number of intraday periods. These are a periodic autoregression and a dynamic factor model. We show the benefits of our approach in the forecasting of district heating demand in a steam network in Germany and aggregate electricity demand in the state of Victoria, Australia. In both studies, accounting for weather …

Bayesian Approaches To Copula Modelling, Michael S. Smith

Michael Stanley Smith

Copula models have become one of the most widely used tools in the applied modelling of multivariate data. Similarly, Bayesian methods are increasingly used to obtain efficient likelihood-based inference. However, to date, there has been only limited use of Bayesian approaches in the formulation and estimation of copula models. This article aims to address this shortcoming in two ways. First, to introduce copula models and aspects of copula theory that are especially relevant for a Bayesian analysis. Second, to outline Bayesian approaches to formulating and estimating copula models, and their advantages over alternative methods. Copulas covered include Archimedean, copulas constructed …