Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
A Comparison Of Microarray Analyses: A Mixed Models Approach Versus The Significance Analysis Of Microarrays, Nathan Wallace Stephens
A Comparison Of Microarray Analyses: A Mixed Models Approach Versus The Significance Analysis Of Microarrays, Nathan Wallace Stephens
Theses and Dissertations
DNA microarrays are a relatively new technology for assessing the expression levels of thousands of genes simultaneously. Researchers hope to find genes that are differentially expressed by hybridizing cDNA from known treatment sources with various genes spotted on the microarrays. The large number of tests involved in analyzing microarrays has raised new questions in multiple testing. Several approaches for identifying differentially expressed genes have been proposed. This paper considers two: (1) a mixed models approach, and (2) the Signiffcance Analysis of Microarrays.
A Logistic Regression Analysis Of Utah Colleges Exit Poll Response Rates Using Sas Software, Clint W. Stevenson
A Logistic Regression Analysis Of Utah Colleges Exit Poll Response Rates Using Sas Software, Clint W. Stevenson
Theses and Dissertations
In this study I examine voter response at an interview level using a dataset of 7562 voter contacts (including responses and nonresponses) in the 2004 Utah Colleges Exit Poll. In 2004, 4908 of the 7562 voters approached responded to the exit poll for an overall response rate of 65 percent. Logistic regression is used to estimate factors that contribute to a success or failure of each interview attempt. This logistic regression model uses interviewer characteristics, voter characteristics (both respondents and nonrespondents), and exogenous factors as independent variables. Voter characteristics such as race, gender, and age are strongly associated with response. …
Bayesian And Positive Matrix Factorization Approaches To Pollution Source Apportionment, Jeff William Lingwall
Bayesian And Positive Matrix Factorization Approaches To Pollution Source Apportionment, Jeff William Lingwall
Theses and Dissertations
The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.