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Uncovering Functional Relationships In Leukemia, Reginald McGee 2017 Mathematical Biosciences Institute

Uncovering Functional Relationships In Leukemia, Reginald Mcgee

Biology and Medicine Through Mathematics Conference

No abstract provided.


Methods For Parameter Estimation Of A Stochastic Seir Model, Kaitlyn Martinez 2017 Colorado School of Mines

Methods For Parameter Estimation Of A Stochastic Seir Model, Kaitlyn Martinez

Biology and Medicine Through Mathematics Conference

No abstract provided.


Using Mathematical Models Of Biological Processes In Genome-Wide Association Studies Of Psychiatric Disorders, Amy Cochran 2017 University of Michigan-Ann Arbor

Using Mathematical Models Of Biological Processes In Genome-Wide Association Studies Of Psychiatric Disorders, Amy Cochran

Biology and Medicine Through Mathematics Conference

No abstract provided.


Performance Of Imputation Algorithms On Artificially Produced Missing At Random Data, Tobias O. Oketch 2017 East Tennessee State University

Performance Of Imputation Algorithms On Artificially Produced Missing At Random Data, Tobias O. Oketch

Electronic Theses and Dissertations

Missing data is one of the challenges we are facing today in modeling valid statistical models. It reduces the representativeness of the data samples. Hence, population estimates, and model parameters estimated from such data are likely to be biased.

However, the missing data problem is an area under study, and alternative better statistical procedures have been presented to mitigate its shortcomings. In this paper, we review causes of missing data, and various methods of handling missing data. Our main focus is evaluating various multiple imputation (MI) methods from the multiple imputation of chained equation (MICE) package in the statistical software ...


Denoising Tandem Mass Spectrometry Data, Felix Offei 2017 East Tennessee State Universtiy

Denoising Tandem Mass Spectrometry Data, Felix Offei

Electronic Theses and Dissertations

Protein identification using tandem mass spectrometry (MS/MS) has proven to be an effective way to identify proteins in a biological sample. An observed spectrum is constructed from the data produced by the tandem mass spectrometer. A protein can be identified if the observed spectrum aligns with the theoretical spectrum. However, data generated by the tandem mass spectrometer are affected by errors thus making protein identification challenging in the field of proteomics. Some of these errors include wrong calibration of the instrument, instrument distortion and noise. In this thesis, we present a pre-processing method, which focuses on the removal of ...


Comparing Methods Of Measuring Chaos In The Symbolic Dynamics Of Strange Attractors, James J. Scully 2017 Georgia State University

Comparing Methods Of Measuring Chaos In The Symbolic Dynamics Of Strange Attractors, James J. Scully

Georgia State Undergraduate Research Conference

No abstract provided.


Statistically Analyzing Assembly Line Processing Times Through Incorporation Of Product Variation, Kyle Rehr, Matthew Farr 2017 Murray State University

Statistically Analyzing Assembly Line Processing Times Through Incorporation Of Product Variation, Kyle Rehr, Matthew Farr

Scholars Week

Timing methods and performance metrics are important in the heavily industrialized world we live in. Industrial plants use metrics to measure quality of production, help make decisions, and drive the strategy of the organization. However, there are many factors to be considered when measuring performance based on a metric; of which we will be analyzing the importance of product variation. We will be analyzing assembly line timings, whilst controlling for product variance, to show the importance differences between products makes in one’s ability to predict performance. In addition, we will be analyzing the current “statistical” methods used by an ...


Application Of Inverse Problems In Imaging, Xiaoyue Luo 2017 Linfield College

Application Of Inverse Problems In Imaging, Xiaoyue Luo

Post-Grant Reports

In this project, we studied how to enhance image quality by denoising and deblurring a given image mathematically. We compared some existing state-of-the-art methods for image denoising and deblurring. We implemented the algorithms numerically using Matlab.

We studied the possibility of combining statistical analysis with the traditional image restoration methods including using wavelets and framelets and we derived some encouraging preliminary results.

My research student Alleta Maier gave a sequence of talks on the project including the Pacific Northwest Mathematical Association of America conference at Oregon State University in April, 2016; Linfield College Taylor Series in March, 2016, and Linfield ...


Moneyball For Creative Writers: A Statistical Strategy For Publishing Your Work, Jon Wesick 2017 Claremont Colleges

Moneyball For Creative Writers: A Statistical Strategy For Publishing Your Work, Jon Wesick

Journal of Humanistic Mathematics

Writers face a challenge getting their poems and stories published. Rather than following the traditional strategy I model creative writing submission as a statistical process and explore the use of numerical metrics to maximize publications.


2014 Reporting Of Sexual Assault: Institutional Comparisons, M. E. Karns 2017 Cornell University

2014 Reporting Of Sexual Assault: Institutional Comparisons, M. E. Karns

Research Studies and Reports

Institutions of higher education are required to submit annual reports of sexual assault crimes to the Department of Education under the Clery Act. The Department of Education makes this data publicly available. Two primary measures are used to assess reporting of assault on campus: the Assault Reporting Ratio (ARR) and the Reporting Rate per 10,000 students (R10K). These measures are easily calculated and can be used to assess practices and policies that impact the reporting of sexual assault on campus.

The ARR and R10K are rate comparisons, a method widely used in public health. These rate comparisons measure how ...


Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan 2017 University of Massachusetts - Amherst

Explorations Into Machine Learning Techniques For Precipitation Nowcasting, Aditya Nagarajan

Masters Theses May 2014 - current

Recent advances in cloud-based big-data technologies now makes data driven solutions feasible for increasing numbers of scientific computing applications. One such data driven solution approach is machine learning where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data. Nowcasting or short-term prediction of rainfall in a given region is an important problem in meteorology. In this thesis we explore the nowcasting problem through a data driven approach by formulating it as a machine learning problem.

State-of-the-art nowcasting systems today are based on numerical models which describe the physical processes leading to ...


Statistics-Bierce Library Study, Tyler J. Hushour 2017 The University of Akron

Statistics-Bierce Library Study, Tyler J. Hushour

Honors Research Projects

This is a report from two surveys that I created and administered to students and faculty at Bierce library who came to the Circulation Desk or the Tech Desk, as well as some of my other findings when periodically looking around the library to see where students like to study or hang-out. There was a written survey given at the Circulation Desk, and a different survey given at the Tech Check-Out Desk. The project is for Melanie Smith-Farrell, the head of Access Services, and is based on a similar study Ian McCullough did in the science library. While this is ...


Inference In Networking Systems With Designed Measurements, Chang Liu 2017 University of Massachusetts Amherst

Inference In Networking Systems With Designed Measurements, Chang Liu

Doctoral Dissertations May 2014 - current

Networking systems consist of network infrastructures and the end-hosts have been essential in supporting our daily communication, delivering huge amount of content and large number of services, and providing large scale distributed computing. To monitor and optimize the performance of such networking systems, or to provide flexible functionalities for the applications running on top of them, it is important to know the internal metrics of the networking systems such as link loss rates or path delays. The internal metrics are often not directly available due to the scale and complexity of the networking systems. This motivates the techniques of inference ...


Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua 2016 New York University School of Medicine

Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua

Philip T. Reiss

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. The core idea is to regress the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, the proposed ...


A Variance Components Model For Statistical Inference On Functional Connectivity Networks, Mark Fiecas, Ivor Cribben, Reyhaneh Bahktiari, Jacqueline Cummine 2016 University of Minnesota

A Variance Components Model For Statistical Inference On Functional Connectivity Networks, Mark Fiecas, Ivor Cribben, Reyhaneh Bahktiari, Jacqueline Cummine

Mark Fiecas

We propose a variance components linear modeling framework for statistical inference on functional connectivity networks that accounts for the temporal autocorrelation inherent in functional magnetic resonance imaging (fMRI) time series data and for the heterogeneity across subjects.  The novel method estimates the former in a nonparametric and subject-specific manner, and estimates the latter using iterative least squares and residual maximum likelihood.  We apply the new model to a resting-state fMRI study to compare the functional connectivity networks in both typical and reading impaired young adults in order to characterize the resting state networks that are related to reading processes. We ...


What Affects Parents’ Choice Of Milk? An Application Of Bayesian Model Averaging, Yingzhe Cheng 2016 University of New Mexico

What Affects Parents’ Choice Of Milk? An Application Of Bayesian Model Averaging, Yingzhe Cheng

Mathematics & Statistics ETDs

This study identifies the factors that influence parents’ choice of milk for their children, using data from a unique survey administered in 2013 in Hunan province, China. In this survey, we identified two brands of milk, which differ in their prices and safety claims by the producer. Data were collected on parents’ choice of milk between the two brands, demographics, attitude towards food safety and behaviors related to food. Stepwise model selection and Bayesian model averaging (BMA) are used to search for influential factors. The two approaches consistently select the same factors suggested by an economic theoretical model, including price ...


An Examination Of The Neural Unreliability Thesis Of Autism, John Butler, Sophie Molholm, Gizely Andrade, John J. Foxe 2016 Dublin Institute of Technology

An Examination Of The Neural Unreliability Thesis Of Autism, John Butler, Sophie Molholm, Gizely Andrade, John J. Foxe

Articles

An emerging neuropathological theory of Autism, referred to here as “the neural unreliability thesis,” proposes greater variability in moment-to-moment cortical representation of environmental events, such that the system shows general instability in its impulse response function. Leading evidence for this thesis derives from functional neuroimaging, a methodology ill-suited for detailed assessment of sensory transmission dynamics occurring at the millisecond scale. Electrophysiological assessments of this thesis, however, are sparse and unconvincing. We conducted detailed examination of visual and somatosensory evoked activity using high-density electrical mapping in individuals with autism (N = 20) and precisely matched neurotypical controls (N = 20), recording large numbers ...


A Multi-Indexed Logistic Model For Time Series, Xiang Liu 2016 East Tennessee State University

A Multi-Indexed Logistic Model For Time Series, Xiang Liu

Electronic Theses and Dissertations

In this thesis, we explore a multi-indexed logistic regression (MILR) model, with particular emphasis given to its application to time series. MILR includes simple logistic regression (SLR) as a special case, and the hope is that it will in some instances also produce significantly better results. To motivate the development of MILR, we consider its application to the analysis of both simulated sine wave data and stock data. We looked at well-studied SLR and its application in the analysis of time series data. Using a more sophisticated representation of sequential data, we then detail the implementation of MILR. We compare ...


Applying Ahp And Clustering Approaches For Public Transportation Decisionmaking: A Case Study Of Isfahan City, Alireza Salavati, Hossein Haghshenas, Bahador Ghadirifaraz, Jamshid Laghaei, Ghodrat Eftekhari 2016 Isfahan Dept. of Transportation and Traffic

Applying Ahp And Clustering Approaches For Public Transportation Decisionmaking: A Case Study Of Isfahan City, Alireza Salavati, Hossein Haghshenas, Bahador Ghadirifaraz, Jamshid Laghaei, Ghodrat Eftekhari

Journal of Public Transportation

The main purpose of this paper is to define appropriate criteria for the systematic approach to evaluate and prioritize multiple candidate corridors for public transport investment simultaneously to serve travel demand, regarding supply of current public transportation system and road network conditions of Isfahan, Iran. To optimize resource allocation, policymakers need to identify proper corridors to implement a public transportation system. In fact, the main question is to adopt the best public transportation system for each main corridor of Isfahan. In this regard, 137 questionnaires were completed by experts, directors, and policymakers of Isfahan to identify goals and objectives in ...


A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz 2016 Washington University in St. Louis

A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz

Doctor of Business Administration Dissertations

At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with ...


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