Physical Sciences and Mathematics Commons^™

Bayesian Semi-Supervised Keyphrase Extraction And Jackknife Empirical Likelihood For Assessing Heterogeneity In Meta-Analysis, Guanshen Wang

Statistical Science Theses and Dissertations

This dissertation investigates: (1) A Bayesian Semi-supervised Approach to Keyphrase Extraction with Only Positive and Unlabeled Data, (2) Jackknife Empirical Likelihood Confidence Intervals for Assessing Heterogeneity in Meta-analysis of Rare Binary Events.

In the big data era, people are blessed with a huge amount of information. However, the availability of information may also pose great challenges. One big challenge is how to extract useful yet succinct information in an automated fashion. As one of the first few efforts, keyphrase extraction methods summarize an article by identifying a list of keyphrases. Many existing keyphrase extraction methods focus on the unsupervised setting, …

Examining Multiple Imputation For Measurement Error Correction In Count Data With Excess Zeros, Shalima Zalsha

Statistical Science Theses and Dissertations

Measurement error and missing data are two common problems in wildlife population surveys. These data are collected from the environment and may be missing or measured with error when the observer’s ability to see the animal is obscured. Methods such as video transects for estimating red snapper abundance and aerial surveys for estimating moose population sizes are highly affected by these problems since total abundance will be underestimated if missing/mismeasured counts are ignored. We shall refer to this problem as visibility bias; it occurs when the true counts are observed when visibility is high, partially observed when visibility is low …

Improved Statistical Methods For Time-Series And Lifetime Data, Xiaojie Zhu

Statistical Science Theses and Dissertations

In this dissertation, improved statistical methods for time-series and lifetime data are developed. First, an improved trend test for time series data is presented. Then, robust parametric estimation methods based on system lifetime data with known system signatures are developed.

In the first part of this dissertation, we consider a test for the monotonic trend in time series data proposed by Brillinger (1989). It has been shown that when there are highly correlated residuals or short record lengths, Brillinger’s test procedure tends to have significance level much higher than the nominal level. This could be related to the discrepancy between …

Time Series Analysis Of Offshore Buoy Light Detection And Ranging (Lidar) Windspeed Data, Aditya Garapati, Charles J. Henderson, Carl Walenciak, Brian T. Waite

SMU Data Science Review

In this paper, modeling techniques for the forecasting of wind speed using historical values observed by Light Detection and Ranging (LIDAR) sensors in an offshore context are described. Both univariate time series and multivariate time series modeling techniques leveraging meteorological data collected simultaneously with the LIDAR data are evaluated for potential contributions to predictive ability. Accurate and timely ability to predict wind values is essential to the effective integration of wind power into existing power grid systems. It allows for both the management of rapid ramp-up / down of base production capacity due to highly variable wind power inputs and …

Compressed Dna Representation For Efficient Amr Classification, John Partee, Robert Hazell, Anjli Solsi, John Santerre

SMU Data Science Review

In this paper, we explore a representation methodology for the compression of DNA isolates. Using lossless string compression via tokenization of frequently repeated segments of DNA, we reduce the length of the isolates to be counted as k-mers for classification. With this new representation, we apply a previously established feature sampling method to dramatically reduce the feature space. In understanding the genetic diversity, we also look at conserving biological function across these spaces. Using a random forest model we were able to predict the resistance or susceptibility of bacteria with 85-90\% accuracy, with a 30-50\% reduction in overall isolate length, …

Cell Assembly Detection In Low Firing-Rate Spike Train Data, Phan Minh Duc Truong

Mathematics Theses and Dissertations

Cell assemblies, defined as groups of neurons forming temporal spike coordination, are thought to be fundamental units supporting major cognitive functions. However, detecting cell assemblies is challenging since they can occur at a range of time scales and with a range of precisions, from synchronous spikes to co-variations in firing rate. In this dissertation, we use a recently published cell assembly detection (CAD) algorithm that is capable of detecting assemblies at a range of time scales and precisions. We first showed that the CAD method can be applied to sparser spike train data than what have previously been reported. This …

Causal Inference And Prediction On Observational Data With Survival Outcomes, Xiaofei Chen

Statistical Science Theses and Dissertations

Infants with hypoplastic left heart syndrome require an initial Norwood operation, followed some months later by a stage 2 palliation (S2P). The timing of S2P is critical for the operation’s success and the infant’s survival, but the optimal timing, if one exists, is unknown. We attempt to estimate the optimal timing of S2P by analyzing data from the Single Ventricle Reconstruction Trial (SVRT), which randomized patients between two different types of Norwood procedure. In the SVRT, the timing of the S2P was chosen by the medical team; thus with respect to this exposure, the trial constitutes an observational study, and …

Evaluation Of The Utility Of Informative Priors In Bayesian Structural Equation Modeling With Small Samples, Hao Ma

Education Policy and Leadership Theses and Dissertations

The estimation of parameters in structural equation modeling (SEM) has been primarily based on the maximum likelihood estimator (MLE) and relies on large sample asymptotic theory. Consequently, the results of the SEM analyses with small samples may not be as satisfactory as expected. In contrast, informative priors typically do not require a large sample, and they may be helpful for improving the quality of estimates in the SEM models with small samples. However, the role of informative priors in the Bayesian SEM has not been thoroughly studied to date. Given the limited body of evidence, specifying effective informative priors remains …

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 …

Sensitivity Analysis For Incomplete Data And Causal Inference, Heng Chen

Statistical Science Theses and Dissertations

In this dissertation, we explore sensitivity analyses under three different types of incomplete data problems, including missing outcomes, missing outcomes and missing predictors, potential outcomes in \emph{Rubin causal model (RCM)}. The first sensitivity analysis is conducted for the \emph{missing completely at random (MCAR)} assumption in frequentist inference; the second one is conducted for the \emph{missing at random (MAR)} assumption in likelihood inference; the third one is conducted for one novel assumption, the ``sixth assumption'' proposed for the robustness of instrumental variable estimand in causal inference.

Forecasting San Francisco Bay Area Rapid Transit (Bart) Ridership, Swee K. Chew, Alec Lepe, Aaron Tomkins, Peter Scheirer

SMU Data Science Review

In this paper, we present a forecasting analysis of the San Francisco Bay Area Rapid Transit (BART) ridership data utilizing a number of different time series methods. BART is a major public transportation system in the Bay Area and it relies heavily on its riders' fares; having models that generate accurate ridership numbers better enables the agency to project revenue and help manage future expenses. For our time series modeling, we utilized autoregressive integrated moving average (ARIMA), deep neural networks (DNN), state space models, and long short-term memory (LSTM) to predict monthly ridership. As there is such a wide range …

Universal Vector Neural Machine Translation With Effective Attention, Joshua Yi, Satish Mylapore, Ryan Paul, Robert Slater

SMU Data Science Review

Neural Machine Translation (NMT) leverages one or more trained neural networks for the translation of phrases. Sutskever intro- duced a sequence to sequence based encoder decoder model which be- came the standard for NMT based systems. Attention mechanisms were later introduced to address the issues with the translation of long sen- tences and improving overall accuracy. In this paper, we propose two improvements to the encoder decoder based NMT approach. Most trans- lation models are trained as one model for one translation. We introduce a neutral/universal model representation that can be used to predict more than one language depending on …

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 …

Demand Forecasting For Alcoholic Beverage Distribution, Lei Jiang, Kristen M. Rollins, Meredith Ludlow, Bivin Sadler

SMU Data Science Review

Forecasting demand is one of the biggest challenges in any business, and the ability to make such predictions is an invaluable resource to a company. While difficult, predicting demand for products should be increasingly accessible due to the volume of data collected in businesses and the continuing advancements of machine learning models. This paper presents forecasting models for two vodka products for an alcoholic beverage distributing company located in the United States with the purpose of improving the company’s ability to forecast demand for those products. The results contain exploratory data analysis to determine the most important variables impacting demand, …

Informal Professional Development On Twitter: Exploring The Online Communities Of Mathematics Educators, Jaymie Ruddock

SMU Journal of Undergraduate Research

Professional development in its most traditional form is a classroom setting with a lecturer and an overwhelming amount of information. It is no surprise, then, that informal professional development away from institutions and on the teacher's own terms is a growing phenomenon due to an increased presence of educators on social media. These communities of educators use hashtags to broadcast to each other, with general hashtags such as #edchat having the broadest audience. However, many math educators usethe hashtags #ITeachMath and #MTBoS, communities I was interested in learning more about. I built a python script that used Tweepy to connect …

Quantitative Model For Setting Manufacturer's Suggested Retail Price, Peter Byrd, Jonathan Knowles, Dmitry Andreev, Jacob Turner, Brian Mente, Laroux Wallace

SMU Data Science Review

In this paper, we present a quantitative approach to model the manufacturer’s suggested retail price (MSRP) for children’s doll- houses and establish relationships among key features that contribute most to establishing MSRP. Determination of the MSRP is a critical step in how consumers respond with their wallets when purchasing an item. KidKraft, a global leader in toys and juvenile products, sets MSRP subjectively using product experts. The process is arduous and time consuming requiring the focus of specialized resources and knowledge of the interaction between key attributes and their impact on consumer value. An accurate prediction of MSRP during the …

Mapping Relationships And Positions Of Objects In Images Using Mask And Bounding Box Data, Jaime M. Villanueva Jr, Anantharam Subramanian, Vishal Ahir, Andrew Pollock

SMU Data Science Review

In this paper we present novel methods for automatically annotating images with relationship and position tags that are derived using mask and bounding box data. A Mask Region-based Convolutional Neural Network (Mask R-CNN) is used as the foundation for the ob- ject detection process. The relationships are found by manipulating the bounding box and mask segmentation outputs of a Mask R-CNN. The absolute positions, the positions of the objects relative to the image, and the relative positions, the positions of objects relative to the other objects, are then associated with the images as annotations that are out- put in order …