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Evaluating Binary Splits On Nominal Inputs, Isaac Xoese Ocloo 2017 University of Texas at El Paso

Evaluating Binary Splits On Nominal Inputs, Isaac Xoese Ocloo

Open Access Theses & Dissertations

The maximally selected statistic approach in building tree models is shown to be a cause of variable selection bias. In this study we propose three methods to solve this problem in building regression trees with nominal predictor variables. Out of the three methods

proposed we explored only two in detail and defer one for further research. We developed an exact method to compute the p-value corresponding to the maximized splitting statistic in regression trees for nominal predictor variables with at most 10 distinct levels and a

method to estimate the best cutoff point as a parameter in a parametric nonlinear …


Estimating The Coefficients Of A Linear Differential Operator, Maria Ivette Barraza 2017 University of Texas at El Paso

Estimating The Coefficients Of A Linear Differential Operator, Maria Ivette Barraza

Open Access Theses & Dissertations

Principal Differential Analysis (PDA; Ramsay, 1996) is used to obtain low dimensional representations of functional data, where each observation is represented as a curve. PDA seeks to identify a Linear Differential Operator (LDO) L = ω0I + ω 1D + ... + ωmDm, where I denotes the identity function and D j the jth derivative, that satisfies as closely as possible that Lx = 0 for each functional observation x. A theorem from analysis establishes that the coefficients of the LDO are in the Sobolev space, and thus can be approximated by B-splines. Current PDA software used to estimate the …


The Nonparametric Estimation Of Elliptical Distributions, Panfeng Liang 2017 University of Texas at El Paso

The Nonparametric Estimation Of Elliptical Distributions, Panfeng Liang

Open Access Theses & Dissertations

In practice, many multivariate datasets have identical marginal distributions. Elliptical distributions can be used to model many of those datasets. In this Thesis, we will propose a Bayesian method using Markov chain Monte Carlo (MCMC) methods to estimate the density function underlying multivariate datasets assuming it is an elliptical distribution.


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

Statistics-Bierce Library Study, Tyler J. Hushour

Williams Honors College, 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 …


Fact Sheet, Yale University Office of Institutional Research 2017 Yale University

Fact Sheet, Yale University Office Of Institutional Research

Fact Sheets

No abstract provided.


Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter 2017 Old Dominion University

Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described …


Comparing Region Level Testing Methods For Differential Dna Methylation Analysis, Arnold Albert Harder 2017 Missouri University of Science and Technology

Comparing Region Level Testing Methods For Differential Dna Methylation Analysis, Arnold Albert Harder

Masters Theses

”Finding possible connections and solutions to help fight progression of diseases is a major area of research. Genomics is a primary path of research in disease research. Through the DNA sequence, possible connections to diseases have been found. However, most methods for fixing issues within a DNA sequence are still out of reach. One potential path is to investigate epigenetic modifications, such as DNA methylation. DNA methylation occurs when a methyl group attached to cytosines on the DNA sequence. Statistical methods can be used to identify sites or regions of significant differences in methylation levels between groups ( e. g. …


Parametric, Nonparametric And Semiparametric Approaches In Profile Monitoring Of Poisson Data, Sepehr Piri 2017 Virginia Commonwealth University

Parametric, Nonparametric And Semiparametric Approaches In Profile Monitoring Of Poisson Data, Sepehr Piri

Theses and Dissertations

Profile monitoring is a relatively new approach in quality control best used when the process data follow a profile (or curve). The majority of previous studies in profile monitoring focused on the parametric modeling of either linear or nonlinear profiles under the assumption of the correct model specification. Our work considers those cases where the parametric model for the family of profiles is unknown or, at least uncertain. Consequently, we consider monitoring Poisson profiles via three methods, a nonparametric (NP) method using penalized splines, a nonparametric (NP) method using wavelets and a semi parametric (SP) procedure that combines both parametric …


An Effective Computational Method Incorporating Multiple Secondary Structure Predictions In Topology Determination For Cryo-Em Images, Abhishek Biswas, Desh Ranjan, Mohammad Zubair, Stephanie Zeil, Kamal Al Nasr, Jing He 2017 Old Dominion University

An Effective Computational Method Incorporating Multiple Secondary Structure Predictions In Topology Determination For Cryo-Em Images, Abhishek Biswas, Desh Ranjan, Mohammad Zubair, Stephanie Zeil, Kamal Al Nasr, Jing He

Computer Science Faculty Publications

A key idea in de novo modeling of a medium-resolution density image obtained from cryo-electron microscopy is to compute the optimal mapping between the secondary structure traces observed in the density image and those predicted on the protein sequence. When secondary structures are not determined precisely, either from the image or from the amino acid sequence of the protein, the computational problem becomes more complex. We present an efficient method that addresses the secondary structure placement problem in presence of multiple secondary structure predictions and computes the optimal mapping. We tested the method using 12 simulated images from alpha-proteins and …


Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon 2017 Georgia Southern University

Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon

College of Graduate Studies: Theses & Dissertations

Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most …


Gamma/Hadron Separation For The Hawc Observatory, Michael J. Gerhardt 2017 Michigan Technological University

Gamma/Hadron Separation For The Hawc Observatory, Michael J. Gerhardt

Dissertations, Master's Theses and Master's Reports

The High-Altitude Water Cherenkov (HAWC) Observatory is a gamma-ray observatory sensitive to gamma rays from 100 GeV to 100 TeV with an instantaneous field of view of ~2 sr. It is located on the Sierra Negra plateau in Mexico at an elevation of 4,100 m and began full operation in March 2015. The purpose of the detector is to study relativistic particles that are produced by interstellar and intergalactic objects such as: pulsars, supernova remnants, molecular clouds, black holes and more. To achieve optimal angular resolution, energy reconstruction and cosmic ray background suppression for the extensive air showers detected by …


Analysis Of Data From A Study To Identify Potential Biomarkers To Indicate Renal Injury, Mitchell D. Tahtinen 2017 Michigan Technological University

Analysis Of Data From A Study To Identify Potential Biomarkers To Indicate Renal Injury, Mitchell D. Tahtinen

Dissertations, Master's Theses and Master's Reports

Ureteropelvic junction obstruction is a disease in which flow from the kidney to the bladder is obstructed for extended periods of time causing irreversible damage to the kidney. Current tests to detect kidney damage caused by obstruction are not effective until significant damage occurs. The purpose of this report is to identify a panel of biomarkers in urine to detect kidney damage earlier by analyzing data collected from a two-part study. Currently, two established urinary biomarkers to indicate kidney damage are NGAL and KIM-1. Biomarkers of interest in this study are CD13, CD10, and CD26. Results from the linear mixed …


On The Equivalence Between Bayesian And Frequentist Nonparametric Hypothesis Testing, Qiuchen Hai 2017 Michigan Technological University

On The Equivalence Between Bayesian And Frequentist Nonparametric Hypothesis Testing, Qiuchen Hai

Dissertations, Master's Theses and Master's Reports

Testing of hypotheses about the population parameter is one of the most fundamental tasks in the empirical sciences and is often conducted by using parametric tests (e.g., the t-test and F-test), in which they assume that the samples are from populations that are normally distributed. When the normality assumption is violated, nonparametric tests are employed as alternatives for making statistical inference. In recent years, the Bayesian versions of parametric tests have been well studied in the literature, whereas in contrast, the Bayesian versions of nonparametric tests are quite scant (for exception, Yuan and Johnson (2008) ) in the literature, mainly …


On Comparative Algorithmic Pathfinding In Complex Networks For Resource-Constrained Software Agents, Michael Moran 2017 Walden University

On Comparative Algorithmic Pathfinding In Complex Networks For Resource-Constrained Software Agents, Michael Moran

Walden Dissertations and Doctoral Studies

Software engineering projects that utilize inappropriate pathfinding algorithms carry a

significant risk of poor runtime performance for customers. Using social network theory,

this experimental study examined the impact of algorithms, frameworks, and map

complexity on elapsed time and computer memory consumption. The 1,800 2D map

samples utilized were computer random generated and data were collected and processed

using Python language scripts. Memory consumption and elapsed time results for each of

the 12 experimental treatment groups were compared using factorial MANOVA to

determine the impact of the 3 independent variables on elapsed time and computer

memory consumption. The MANOVA indicated a …


Estimating The Effects Of Overstory Retention, Vegetative Competition, And Site Quality On The Height Growth Of Small Ponderosa Pine Trees Using Regression Quantiles, Colin P. Kirkmire 2017 University of Montana, Missoula

Estimating The Effects Of Overstory Retention, Vegetative Competition, And Site Quality On The Height Growth Of Small Ponderosa Pine Trees Using Regression Quantiles, Colin P. Kirkmire

Graduate Student Theses, Dissertations, & Professional Papers

Ponderosa pine (Pinus ponderosa C. Lawson) forests in the Inland Northwestern region of the US are increasingly managed under multi-aged silvicultural systems that provide stand structure for wildlife habitat, timber production, enhanced aesthetic, or restoration of presettlement conditions (O'Hara 2005). Partial retention harvest, where an element of the previous stand's overstory structure is retained, is commonly used to achieve a multi-aged stand structure. However, little is known about how ponderosa pine trees in the understory respond to overstory and understory competitive factors following partial retention harvest. The height growth of small trees was hypothesized to be influenced by site …


Juvenile Remains: Predicting Body Mass And Stature In Modern American Populations, Erin F E Pinkston 2017 Humboldt State University

Juvenile Remains: Predicting Body Mass And Stature In Modern American Populations, Erin F E Pinkston

Cal Poly Humboldt theses and projects

There are increasing numbers of unidentified persons in the U.S. and abroad. To generate positive identifications, forensic anthropologists and others working in the medicolegal field employ a variety of methods to produce biological profiles to match to case files and missing persons databases. Body mass, and stature are two important components of a biological profile, and both can be estimated using regression formulae derived from skeletal metrics. In cases of unidentified juvenile remains, these are particularly important metrics, as it is difficult or impossible to determine sex in prepubescent remains, and the quality of ancestry estimation is currently under debate …


The Use Of The Pivot Pairwise Relative Criteria Importance Assessment Method For Determining The Weights Of Criteria, Florentin Smarandache, Dragisa Stanujkic, Edmundas Kazimieras Zavadskas, Darjan Karabasevic, Zenonas Turskis 2017 University of New Mexico

The Use Of The Pivot Pairwise Relative Criteria Importance Assessment Method For Determining The Weights Of Criteria, Florentin Smarandache, Dragisa Stanujkic, Edmundas Kazimieras Zavadskas, Darjan Karabasevic, Zenonas Turskis

Branch Mathematics and Statistics Faculty and Staff Publications

The weights of evaluation criteria could have a significant impact on the results obtained by applying multiple criteria decision-making methods. Therefore, the two extensions of the SWARA method that can be used in cases when it is not easy, or even is impossible to reach a consensus on the expected importance of the evaluation criteria are proposed in this paper. The primary objective of the proposed extensions is to provide an understandable and easy-to-use approach to the collecting of respondents’ real attitudes towards the significance of evaluation criteria and to also provide an approach to the checking of the reliability …


Predicting Individualized Treatment Effects Via Random Forests Of Interaction Trees, Annette Pena Franco 2017 University of Texas at El Paso

Predicting Individualized Treatment Effects Via Random Forests Of Interaction Trees, Annette Pena Franco

Open Access Theses & Dissertations

Abstract Not Available


Analysis Of Bias-Corrected And Exact Estimators For Binomial Generalized Linear Model Parameters, Hamna Hannan 2017 University of Texas at El Paso

Analysis Of Bias-Corrected And Exact Estimators For Binomial Generalized Linear Model Parameters, Hamna Hannan

Open Access Theses & Dissertations

Typically, small samples have always been a problem for binomial generalized linear models. Though generalized linear models are widely popular in public health, social sciences etc. In small sample scenarios the non-existence of the maximum likelihood (ML) estimators is very common as well as separation occurs in the data. In logistic regression the maximum likelihood estimates are found to have biased away from origin. My work examines the bias-reduced and exact estimators that have been used to estimate the slope parameters and standard errors of the estimated slope parameters as compared to the traditional ML method.

The present work is …


Longitudinal And Geographical Modeling Of Circular Data With An Application To Sudden Infant Death Syndrome, Xinyan Cai 2017 University of South Carolina

Longitudinal And Geographical Modeling Of Circular Data With An Application To Sudden Infant Death Syndrome, Xinyan Cai

Theses and Dissertations

The aim of this thesis is to study seasonality of death in U.S. infants who died from SIDS. We also propose to investigate secular trends and geographical patterns of seasonal patterns of mortality. The application of circular statistics is used to describe the seasonality of the month of death in infants who died from SIDS in 1990, 2000 and 2010. The secular trends of seasonal patterns of SIDS mortality are investigated using a circular linear regression model after adjusting for potential confounders. The geographical variation in seasonal patterns of SIDS mortality is explored from the U.S. map and quantified by …


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