Physical Sciences and Mathematics Commons^™

Development Of A Bayesian Joint Logistic Model To Better Study The Association Between Haplotypes And Disease, Anthony M. D'Amelio Jr

Dissertations & Theses (Open Access)

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality.

In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any …

Biogeochemical Response Of Alpine Lakes To A Recent Increase In Dust Deposition In The Southwestern, Us, Ashley P. Ballantyne, Janice Brahney, C. L. Lawrence, J. Saros, Jason C. Neff

Watershed Sciences Faculty Publications

The deposition of dust has recently increased significantly over some regions of the western US. Here we explore how changes in dust deposition have affected the biogeochemistry of two alpine watersheds in Colorado, US. We first reconstruct recent changes in the mass accumulation rate of sediments and then we use isotopic measurements in conjunction with a Bayesian mixing model to infer that approximately 95% of the inorganic fraction of lake sediments is derived from dust. Elemental analyses of modern dust indicate that dust is enriched in Ca, Cr, Cu, Mg, Ni, and in one watershed, Fe and P relative to …

Hierarchical Probit Models For Ordinal Ratings Data, Allison M. Butler

Theses and Dissertations

University students often complete evaluations of their courses and instructors. The evaluation tool typically contains questions about the course and the instructor on an ordinal Likert scale. We assess instructor effectiveness while adjusting for known confounders. We present a probit regression model with a latent variable to measure the instructor effectiveness accounting for student specific covariates, such as student grade in the course, high school and university GPA, and ACT score.

Adaptive Threat Detector Testing Using Bayesian Gaussian Process Models, Bradley Thomas Ferguson

Theses and Dissertations

Detection of biological and chemical threats is an important consideration in the modern national defense policy. Much of the testing and evaluation of threat detection technologies is performed without appropriate uncertainty quantification. This paper proposes an approach to analyzing the effect of threat concentration on the probability of detecting chemical and biological threats. The approach uses a probit semi-parametric formulation between threat concentration level and the probability of instrument detection. It also utilizes a bayesian adaptive design to determine at which threat concentrations the tests should be performed. The approach offers unique advantages, namely, the flexibility to model non-monotone curves …

Bayesian Mixtures Of Autoregressive Models, Sally Wood, Ori Rosen, Robert Kohn

Sally Wood

In this paper we propose a class of time-domain models for analyzing possibly nonstationary time series. This class of models is formed as a mixture of time series models, whose mixing weights are a function of time. We consider specifically mixtures of autoregressive models with a common but unknown lag. The model parameters, including the number of mixture components, are estimated via Markov chain Monte Carlo methods. The methodology is illustrated with simulated and real data.

Inference Without Significance: Measuring Support For Hypotheses Rather Than Rejecting Them, Tim Gerrodette

United States Department of Commerce: Staff Publications

Despite more than half a century of criticism, significance testing continues to be used commonly by ecologists. Significance tests are widely misused and misunderstood, and even when properly used, they are not very informative for most ecological data. Problems of misuse and misinterpretation include: (i) invalid logic; (ii) rote use; (iii) equating statistical significance with biological importance; (iv) regarding the P-value as the probability that the null hypothesis is true; (v) regarding the P-value as a measure of effect size; and (vi) regarding the P-value as a measure of evidence. Significance tests are poorly suited for inference because they pose …

Development And Implementation Of A Bayesian Model For Sediment Transport In Fluvial Systems, Mark Schmelter

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Recent studies in the field of fluvial sediment transport underscore the difficulty in reliably estimating transport model parameters, collecting accurate observations, and making predictions due to measurement error and conceptual model uncertainty. There is a pressing need to develop models that can account for measurement error, conceptual model uncertainty, and natural variability while providing probability-based predictions as well as a means for conceptual model discrimination. The model presented in this research employs an excess shear sediment transport equation for a uni-size sediment bed developed in a Bayesian statistical framework. This statistical model provides a means to rigorously estimate distributions of …

A Partial Order On Classical And Quantum States, Arka Bandyopadhyay

LSU Master's Theses

In this work we extend the work done by Bob Coecke and Keye Martin in their paper “Partial Order on Classical States and Quantum States (2003)”. We review basic notions involving elementary domain theory, the set of probability measures on a finite set {a1, a2, ..., an}, which we identify with the standard (n-1)-simplex ∆n and Shannon Entropy. We consider partial orders on ∆n, which have the Entropy Reversal Property (ERP) : elements lower in the order have higher (Shannon) entropy or equivalently less information . The ERP property is important because of its applications in quantum information theory. We …