Statistical Models And Analysis Of Univariate And Multivariate Degradation Data,
2020
Southern Methodist University
Statistical Models And Analysis Of Univariate And Multivariate Degradation Data, Lochana Palayangoda
Statistical Science Theses and Dissertations
For degradation data in reliability analysis, estimation of the first-passage time (FPT) distribution to a threshold provides valuable information on reliability characteristics. Recently, Balakrishnan and Qin (2019; Applied Stochastic Models in Business and Industry, 35:571-590) studied a nonparametric method to approximate the FPT distribution of such degradation processes if the underlying process type is unknown. In this thesis, we propose improved techniques based on saddlepoint approximation, which enhance upon their suggested methods. Numerical examples and Monte Carlo simulation studies are used to illustrate the advantages of the proposed techniques. Limitations of the improved techniques are discussed and some possible solutions …
Statistical Inference Of Adaptation At Multiple Genomic Scales Using Supervised Classification And A Hidden Markov Model,
2020
Duquesne University
Statistical Inference Of Adaptation At Multiple Genomic Scales Using Supervised Classification And A Hidden Markov Model, Lauren A. Sugden
Biology and Medicine Through Mathematics Conference
No abstract provided.
The Primary Volatile Composition Of Comet C/2015 Er61 (Panstarrs),
2020
University of Missouri-St. Louis
The Primary Volatile Composition Of Comet C/2015 Er61 (Panstarrs), Aaron Butler
Theses
In the outer edges of the solar system exist two regions: the Kuiper belt and Oort cloud. These two regions have a high amount of icy bodies (comets) orbiting the Sun. Comets located within the Oort cloud and Kuiper belt contain an ancient codex to the solar systems contents, before the formation of our solar system. Presented are near-infrared, high-resolution (λ/Δλ ~40000) data obtained from the immersion-grating echelle spectrograph iSHELL at the 3m NASA Infrared Telescope Facility (IRTF) in Maunakea, Hawaii of the Oort cloud comet C/2015 ER61 (PANSTARRS). Observations took place on April 15 and 17 in 2017 while …
Predictive Modeling Of Asynchronous Event Sequence Data,
2020
Louisiana State University
Predictive Modeling Of Asynchronous Event Sequence Data, Jin Shang
LSU Doctoral Dissertations
Large volumes of temporal event data, such as online check-ins and electronic records of hospital admissions, are becoming increasingly available in a wide variety of applications including healthcare analytics, smart cities, and social network analysis. Those temporal events are often asynchronous, interdependent, and exhibiting self-exciting properties. For example, in the patient's diagnosis events, the elevated risk exists for a patient that has been recently at risk. Machine learning that leverages event sequence data can improve the prediction accuracy of future events and provide valuable services. For example, in e-commerce and network traffic diagnosis, the analysis of user activities can be …
Decision Tree For Predicting The Party Of Legislators,
2020
CUNY New York City College of Technology
Decision Tree For Predicting The Party Of Legislators, Afsana Mimi
Publications and Research
The motivation of the project is to identify the legislators who voted frequently against their party in terms of their roll call votes using Office of Clerk U.S. House of Representatives Data Sets collected in 2018 and 2019. We construct a model to predict the parties of legislators based on their votes. The method we used is Decision Tree from Data Mining. Python was used to collect raw data from internet, SAS was used to clean data, and all other calculations and graphical presentations are performed using the R software.
Modeling Species Distribution And Habitat Suitability Of American Ginseng (Panax Quinquefolius) In Virginia,
2020
James Madison University
Modeling Species Distribution And Habitat Suitability Of American Ginseng (Panax Quinquefolius) In Virginia, Jacob D. J. Peters
Masters Theses, 2020-current
American ginseng (Panax quinquefolius) is a well-known and sought-after medicinal plant native to North America that is facing increased threat of extinction due to overharvesting, herbivory, and habitat loss. Species distribution and habitat suitability models may be valuable to landowners interested in sustainable harvest or to institutions interested in the conservation and restoration of the species. With unequal sampling efforts across a region of interest, it is likely that some locations with appropriate habitat may be misrepresented in model predictions. This study refined a state-derived species distribution model for ginseng through increased sampling effort across the Cumberland Plateau …
An Analysis Of Dredge Efficiency For Surfclam And Ocean Quahog Commercial Dredges,
2020
The University of Southern Mississippi
An Analysis Of Dredge Efficiency For Surfclam And Ocean Quahog Commercial Dredges, Leanne Poussard
Master's Theses
Between 1997 and 2011, The National Marine Fisheries Service conducted 50 depletion experiments to estimate survey gear efficiency and stock density for Atlantic surfclam (Spisula solidissima) and ocean quahog (Arctica islandica) populations using commercial hydraulic dredges. The Patch Model was formulated to estimate gear efficiency and organism density from the data. The range of efficiencies estimated is substantial, leading to uncertainty in the application of these estimates in stock assessment. Analysis of depletion experiment simulations showed that uncertainty in the estimates of gear efficiency from depletion experiments was reduced by higher numbers of dredge tows per experiment, more tow overlap …
Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data,
2020
Washington University in St. Louis
Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim
McKelvey School of Engineering Graduate Student Theses & Dissertations
Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is …
Analyzing Competitive Balance In Professional Sport,
2020
University of Connecticut - Storrs
Analyzing Competitive Balance In Professional Sport, Kevin Alwell
Honors Scholar Theses
In this paper we review several measures to statistically analyze competitive balance and report which leagues have a wider variance of performance amongst its competitors. Each league seeks to maintain high levels of parity, making matches and overall season more unpredictable and appealing to the general audience. Here we quantify competitive advantage across major sports leagues in numbers using several statistical methods in order for leagues to optimize their revenue.
Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation.,
2020
University of Louisville
Novel Inference Methods For Generalized Linear Models Using Shrinkage Priors And Data Augmentation., Arinjita Bhattacharyya
Electronic Theses and Dissertations
Generalized linear models have broad applications in biostatistics and sociology. In a regression setup, the main target is to find a relevant set of predictors out of a large collection of covariates. Sparsity is the assumption that only a few of these covariates in a regression setup have a meaningful correlation with an outcome variate of interest. Sparsity is incorporated by regularizing the irrelevant slopes towards zero without changing the relevant predictors and keeping the resulting inferences intact. Frequentist variable selection and sparsity are addressed by popular techniques like Lasso, Elastic Net. Bayesian penalized regression can tackle the curse of …
Applications Of Machine Learning In High-Frequency Trade Direction Classification,
2020
Utah State University
Applications Of Machine Learning In High-Frequency Trade Direction Classification, Jared E. Hansen
All Graduate Theses and Dissertations, Spring 1920 to Summer 2023
The correct assignment of trades as buyer-initiated or seller-initiated is paramount in many quantitative finance studies. Simple decision rule methods have been used for signing trades since many data sets available to researchers do not include the sign of each trade executed. By utilizing these decision rule methods, as well as engineering new variables from available data, we have demonstrated that machine learning models outperform prior methods for accurately signing trades as buys and sells, achieving state-of-the-art results. The best model developed was 4.5 percentage points more accurate than older methods when predicting onto unseen data. Since finance and economics …
483— Effectiveness Of Mmr Vaccination In Orthodox Jewish Neighborhoods,
2020
SUNY Geneseo
483— Effectiveness Of Mmr Vaccination In Orthodox Jewish Neighborhoods, Meenu Mundackal
GREAT Day Posters
Measles is a highly contagious disease, where large outbreaks arise by direct contact between susceptible (unvaccinated) and infectious individuals. Many Orthodox Jewish neighborhoods were affected by measles from 2018-2019. To quantify the vaccination effort on this susceptible population, a retrospective analysis was used to study the NYC and Rockland County populations using a differential equations model. A subsequent model, known as a realistically-structured network model, studied only the NYC population, in relation to typical household size. Vaccination strategies were applied to three cohorts: unvaccinated family members, members with 1 prior MMR dose, and members with 2 prior MMR doses. The …
484— Modeling Social Distancing Methods And Their Effectiveness In Combating The Spread Of Ebola,
2020
SUNY Geneseo
484— Modeling Social Distancing Methods And Their Effectiveness In Combating The Spread Of Ebola, Rachel Fair
GREAT Day Posters
Ebola Virus Disease (EVD) is a rare but severe disease that is transmitted among humans through direct-contact with, and close proximity to, infected bodily fluids. From 2014-16, West Africa experienced the largest Ebola outbreak ever recorded, infecting over 28,000 people, and killing over 11,000. Although the symptoms of EVD are treatable, the disease can be extremely deadly, with an average of 50% EVD cases resulting in fatality. In areas where healthcare is scarce and vaccinations are not readily available, the practices of social distancing and self-quarantining have been shown to be highly effective in combating the spread of EVD. To …
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach,
2020
Southern Methodist University
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
SMU Data Science Review
In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied …
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring,
2020
Kennesaw State University
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring, Yan Wang
Doctor of Data Science and Analytics Dissertations
In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) …
Interdependence Across Foreign Exchange Rate Markets- A Mixed Copula Approach,
2020
Western Kentucky University
Interdependence Across Foreign Exchange Rate Markets- A Mixed Copula Approach, Richard Adjei-Boateng
Masters Theses & Specialist Projects
The purpose of this thesis is to study the dependence structure of exchange rate pairs using a mixture of copula as opposed to a single copula approach. Mixed copula models have the ability to generate dependence structures that do not belong to existing copula families. The flexibility in choosing component copulas in this mixture model aids the construction of a system that is simultaneously parsimonious and flexible enough to generate most dependence patterns in exchange rate data. Furthermore, the method of mixture copulas facilitates the separation of both the structure and degree of dependence, concepts that are respectively embodied in …
A Monte Carlo Analysis Of Standard Error-Based Methods For Computing Confidence Intervals,
2020
Western Kentucky University
A Monte Carlo Analysis Of Standard Error-Based Methods For Computing Confidence Intervals, Elayna Wichert
Masters Theses & Specialist Projects
The objective of this study is to empirically test existing techniques to calculate the likely range of values for a Classical Test Theory true score given an observed score. The traditional method for forming these confidence intervals has used the standard error of measurement (SEM) as the basis for this confidence interval. An alternate equation, the standard error of estimate (SEE), has been recommended in place of the SEM for this purpose, yet it remains overlooked in the field of psychometrics. It is important that the correct equation be used in various applications in personnel psychology. Monte Carlo analyses were …
Boom Or Bust: Examining The Relationship Between High School Recruiting Rankings And The Nfl Draft,
2020
University of South Carolina
Boom Or Bust: Examining The Relationship Between High School Recruiting Rankings And The Nfl Draft, Nicholas E. Tice
Senior Theses
The goal of this thesis is to model the probability of a high school football player’s chance of being drafted based on information taken from their recruiting profile. The response variable is binary and defined as drafted (1) or undrafted (0). The independent variables were collected by scraping data from the recruiting websites including height, weight, position, hometown, recruiting grade and other socioeconomic factors based on the player’s high school. 247Sports and ESPN were the two recruiting services used and compared in this study. Because of the binary nature of the dependent variable, logistic regression and decision trees were chosen …
Inferences For Weibull-Gamma Distribution In Presence Of Partially Accelerated Life Test,
2020
Al-Azhar University - Egypt
Inferences For Weibull-Gamma Distribution In Presence Of Partially Accelerated Life Test, Mahmoud Mansour, M A W Mahmoud Prof., Rashad El-Sagheer
Basic Science Engineering
In this paper, the point at issue is to deliberate point and interval estimations for the parameters of Weibull-Gamma distribution (WGD) using progressively Type-II censored (PROG-II-C) sample under step stress partially accelerated life test (SSPALT) model. The maximum likelihood (ML), Bayes, and four parametric bootstrap methods are used to obtain the point estimations for the distribution parameters and the acceleration factor. Furthermore, the approximate confidence intervals (ACIs), four bootstrap confidence intervals and credible intervals of the estimators have been gotten. The results of Bayes estimators are computed under the squared error loss (SEL) function using Markov Chain Monte Carlo (MCMC) …
Measuring Localization Confidence For Quantifying Accuracy And Heterogeneity In Single-Molecule Super-Resolution Microscopy,
2020
Washington University in St. Louis
Measuring Localization Confidence For Quantifying Accuracy And Heterogeneity In Single-Molecule Super-Resolution Microscopy, Hesam Mazidi, Tianben Ding, Arye Nehorai, Matthew D. Lew
Electrical & Systems Engineering Publications and Presentations
We present a computational method, termed Wasserstein-induced flux (WIF), to robustly quantify the accuracy of individual localizations within a single-molecule localization microscopy (SMLM) dataset without ground- truth knowledge of the sample. WIF relies on the observation that accurate localizations are stable with respect to an arbitrary computational perturbation. Inspired by optimal transport theory, we measure the stability of individual localizations and develop an efficient optimization algorithm to compute WIF. We demonstrate the advantage of WIF in accurately quantifying imaging artifacts in high-density reconstruction of a tubulin network. WIF represents an advance in quantifying systematic errors with unknown and complex distributions, …
