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2016

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Full-Text Articles in Other Statistics and Probability

Tutorial For Using The Center For High Performance Computing At The University Of Utah And An Example Using Random Forest, Stephen Barton Dec 2016

Tutorial For Using The Center For High Performance Computing At The University Of Utah And An Example Using Random Forest, Stephen Barton

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

Random Forests are very memory intensive machine learning algorithms and most computers would fail at building models from datasets with millions of observations. Using the Center for High Performance Computing (CHPC) at the University of Utah and an airline on-time arrival dataset with 7 million observations from the U.S. Department of Transportation Bureau of Transportation Statistics we built 316 models by adjusting the depth of the trees and randomness of each forest and compared the accuracy and time each took. Using this dataset we discovered that substantial restrictions to the size of trees, observations allowed for each tree, and variables …


A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz Dec 2016

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 …


A Comparison Of Techniques For Handling Missing Data In Longitudinal Studies, Alexander R. Bogdan Nov 2016

A Comparison Of Techniques For Handling Missing Data In Longitudinal Studies, Alexander R. Bogdan

Masters Theses

Missing data are a common problem in virtually all epidemiological research, especially when conducting longitudinal studies. In these settings, clinicians may collect biological samples to analyze changes in biomarkers, which often do not conform to parametric distributions and may be censored due to limits of detection. Using complete data from the BioCycle Study (2005-2007), which followed 259 premenopausal women over two menstrual cycles, we compared four techniques for handling missing biomarker data with non-Normal distributions. We imposed increasing degrees of missing data on two non-Normally distributed biomarkers under conditions of missing completely at random, missing at random, and missing not …


Probabilistic Methods In Information Theory, Erik W. Pachas Sep 2016

Probabilistic Methods In Information Theory, Erik W. Pachas

Electronic Theses, Projects, and Dissertations

Given a probability space, we analyze the uncertainty, that is, the amount of information of a finite system, by studying the entropy of the system. We also extend the concept of entropy to a dynamical system by introducing a measure preserving transformation on a probability space. After showing some theorems and applications of entropy theory, we study the concept of ergodicity, which helps us to further analyze the information of the system.


Spot Volatility Estimation Of Ito Semimartingales Using Delta Sequences, Weixuan Gao May 2016

Spot Volatility Estimation Of Ito Semimartingales Using Delta Sequences, Weixuan Gao

Arts & Sciences Electronic Theses and Dissertations

This thesis studies a unifying class of nonparametric spot volatility estimators proposed by Mancini et. al.(2013). This method is based on delta sequences and is conceived to include many of the existing estimators in the field as special cases. The thesis first surveys the asymptotic theory of the proposed estimators under an infill asymptotic scheme and fixed time horizon, when the state variable follows a Brownian semimartingale. Then, some extensions to include jumps and financial microstructure noise in the observed price process are also presented. The main goal of the thesis is to assess the suitability of the proposed methods …


The Relationship Between Time Of Day, Mood, And Electroencephalography (Eeg) Asymmetry, Morgan Tantillo May 2016

The Relationship Between Time Of Day, Mood, And Electroencephalography (Eeg) Asymmetry, Morgan Tantillo

Honors Projects

Previous researchers have had success in finding a correlation between exercise and an increase in positive mood. Researchers have also found a correlation between time of day and mood. The current study will explore the relationship between time of day, mood, and electroencephalography (EEG) asymmetry. The study utilized a convenient sample of ten undergraduate students at Bowling Green State University. Participants had baseline EEG recordings taken, and then participated in moderate exercise, followed by another EEG recording. Participants’ mood was assessed through a self-reported mood questionnaire before the condition as well as immediately after. Due to multiple statistical tests, the …


Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, Thomas K. Torku May 2016

Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, Thomas K. Torku

Electronic Theses and Dissertations

The evolution of big data has led to financial time series becoming increasingly complex, noisy, non-stationary and nonlinear. Takens theorem can be used to analyze and forecast nonlinear time series, but even small amounts of noise can hopelessly corrupt a Takens approach. In contrast, Singular Spectrum Analysis is an excellent tool for both forecasting and noise reduction. Fortunately, it is possible to combine the Takens approach with Singular Spectrum analysis (SSA), and in fact, estimation of key parameters in Takens theorem is performed with Singular Spectrum Analysis. In this thesis, we combine the denoising abilities of SSA with the Takens …


A New Right Tailed Test Of The Ratio Of Variances, Elizabeth Rochelle Lesser Jan 2016

A New Right Tailed Test Of The Ratio Of Variances, Elizabeth Rochelle Lesser

UNF Graduate Theses and Dissertations

It is important to be able to compare variances efficiently and accurately regardless of the parent populations. This study proposes a new right tailed test for the ratio of two variances using the Edgeworth’s expansion. To study the Type I error rate and Power performance, simulation was performed on the new test with various combinations of symmetric and skewed distributions. It is found to have more controlled Type I error rates than the existing tests. Additionally, it also has sufficient power. Therefore, the newly derived test provides a good robust alternative to the already existing methods.


Monte Carlo Approx. Methods For Stochastic Optimization, John Fowler Jan 2016

Monte Carlo Approx. Methods For Stochastic Optimization, John Fowler

Pomona Senior Theses

This thesis provides an overview of stochastic optimization (SP) problems and looks at how the Sample Average Approximation (SAA) method is used to solve them. We review several applications of this problem-solving technique that have been published in papers over the last few years. The number and variety of the examples should give an indication of the usefulness of this technique. The examples also provide opportunities to discuss important aspects of SPs and the SAA method including model assumptions, optimality gaps, the use of deterministic methods for finite sample sizes, and the accelerated Benders decomposition algorithm. We also give a …