Cancerous Male and Female Gene Expression

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Clarissa Farmer and E. Shannon Tass, Statistics Genetic diagnosing is becoming more popular, as well as more and more accurate. However, many genetic diseases have complex genetic effects and are still not fully understood. Transthyretin Amyloidosis (ATTR; also known as familial or hereditary amyloidosis) is a terminal genetic disease. It is caused by unstable transthyretin […]

Cluster Analysis via Random Partition Distributions

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Brandon Carter, Dr. David B. Dahl, Department of Statistics Introduction Cluster analysis is an important exploratory data analysis technique used in a wide variety of fields. Cluster analysis seeks to discover a natural grouping of the data, where items in the same cluster or group are more similar than items from different clusters. Through our research, […]

Estimating Seasonal Onsets and Peaks of Bronchiolitis with Temporally Uncertain Data

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Sierra Pugh and Dr. Matthew Heaton, Department of Statistics Introduction Bronchiolitis (an acute lower respiratory tract viral infection in infants) is the most common cause of infant hospitalizations in the United States. The only preventative intervention currently available is monthly injections of immunoprophylaxis. However, this treatment is expensive and needs to be administered simultaneously with […]

Validating Remote Sensing Temperatures for Scientific Use

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Gavin Collins and Dr. Matthew Heaton, Department of Statistics Introduction Satellite remote-sensing is often used to collect atmospheric data, providing insight into climate variability over large regions of the earth. Common issues with such data include (i) missing information due to cloud cover at the time of a satellite passing, and (ii) large blocks of […]

Identifying Risk Factors for Interstate Crashes using Spatial Statistics

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Gibson, Kaitlin Identifying Risk Factors for Interstate Crashes using Spatial Statistics Faculty Mentor: Matthew Heaton, BYU Department of Statistics Introduction The goal of systemic highway safety improvement is to identify road characteristics, called risk factors, associated with a higher prevalence of crashes, so that the roads can be modified to avoid these characteristics. However, the […]

An Algorithm for Multiple Regression Variable Selection for Biochemical Oxygen Demand

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Zach White An Algorithm for Multiple Regression Variable Selection for Biochemical Oxygen Demand Dr. William Christensen Department of Statistics Introduction Biochemical Oxygen Demand (BOD) is used to measure of the amount of oxygen required by aerobic bacteria and other microorganisms to stabilize decomposable organic matter. It is run as a laboratory based biodegradation test and […]

Bayesian Model for Antarctic Accumulation and Proposing Field Measurement

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Philip White and Shane Reese, Statistics Introduction Antarctica’s significance to the global climate is due to the vast amounts of water stored in its ice sheet. Indeed, its ice sheet stores enough water to increase the global sea level by about 200 feet if it were to melt. Even though radical climate change could not […]

Gaussian Process Modeling of Modern Mass Spectrometry Computer Experimental Data

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Mickey Warner and C. Shane Reese, Statistics Introduction A new mass spectrometry technique (VENDAMS) has been developed to allow the quantification of rate constants for complicated chemical reactions. Due to the expensive nature of the method, computer experiments designed to solve a set of equations provide supplemental information to the process. The computer simulator takes […]

Modeling Supernovae Light Curves: A Vital Step Toward Photometric Classification

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Brittany Spencer and Dr. Shane Reese, Statistics Department Introduction Type Ia supernovae play a critical role in understanding the nature of the evolving universe. The Dark Energy Survey is currently investigating the expansion rate of the universe, with the intent to gain insight into the nature of dark energy. One of their primary measures of […]

A Bayesian Nonparametric Approach to Hyperspectral Data Analysis

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Jessica Seeger and Dr. Candace Berrett, BYU Department of Statistics Introduction Hyperspectral imaging (HSI) is a technology that provides a dense set of previously un- available data{o ering the opportunity for use in a variety of applications such as food safety, ecology, and non-proliferation research. HSI stores measurements across three dimen- sions (two-dimensional space and […]