Longitudinal Data Analysis and Time Series Commons^™

Data-Driven Analysis Of Drug And Substance Abuse Rates Across The Varying Regions In The United States Of America, Reem Saleh

University Honors Theses

Drugs and substance abuse is one of the leading causes of death for adolescents in the United States. The consequences of using these drugs are profound and can cause both damage to one's physical and psychological health. The rates of drug abuse in the United States continue to increase over the years. This paper analyzes the trends in rates of drug abuse in the four regions in the United States. It looks at the rates in cocaine, cigarettes, marijuana, and tobacco. A preliminary analysis was done to look at the trend in rates followed by an ARIMA time series ...

Intervention Time Series Analysis Of Organ Donor Transplants In The Us, Supraja Malladi

Biology and Medicine Through Mathematics Conference

No abstract provided.

Penalized Estimation Of Autocorrelation, Xiyan Tan

All Dissertations

This dissertation explored the idea of penalized method in estimating the autocorrelation (ACF) and partial autocorrelation (PACF) in order to solve the problem that the sample (partial) autocorrelation underestimates the magnitude of (partial) autocorrelation in stationary time series. Although finite sample bias corrections can be found under specific assumed models, no general formulae are available. We introduce a novel penalized M-estimator for (partial) autocorrelation, with the penalty pushing the estimator toward a target selected from the data. This both encapsulates and differs from previous attempts at penalized estimation for autocorrelation, which shrink the estimator toward the target value of zero ...

Sparse Model Selection Using Information Complexity, Yaojin Sun

Doctoral Dissertations

This dissertation studies and uses the application of information complexity to statistical model selection through three different projects. Specifically, we design statistical models that incorporate sparsity features to make the models more explanatory and computationally efficient.

In the first project, we propose a Sparse Bridge Regression model for variable selection when the number of variables is much greater than the number of observations if model misspecification occurs. The model is demonstrated to have excellent explanatory power in high-dimensional data analysis through numerical simulations and real-world data analysis.

The second project proposes a novel hybrid modeling method that utilizes a mixture ...

Are Long-Period Exoplants Around Cool Stars More Common Than We Thought?, Emily Jane Safron

LSU Doctoral Dissertations

The Kepler mission has been the catalyst for discovery of nearly 5,000 confirmed and candidate exoplanets. The majority of these candidates orbit Sun-like stars, and have orbital periods comparable to or shorter than that of the Earth, due to the selection bias inherent in the transit method and the limitations of automated transit search algorithms. We aim to develop a richer understanding of the population of exoplanets around the lowest-mass stars, the M spectral type. We are particularly interested in exoplanets with long orbital periods, which are difficult or impossible to find using standard transit search algorithms. In our ...

Slices Of The Big Apple: A Visual Explanation And Analysis Of The New York City Budget, Joanne Ramadani

Dissertations, Theses, and Capstone Projects

As a component of government, budgets are fundamental not only to improving the quality of a shared society, but also to understanding what our government officials consider to be their priorities. However, most budgets can be difficult to understand, using terms that are not familiar to people who have not studied finance or economics. To that end, Slices of the Big Apple is an interactive, centralized narrative website that uses visualizations at its core in order to: 1) facilitate a holistic understanding of the New York City government budget for NYC residents; and 2) conduct a five-year analysis of Community ...

Lake Huron Shoreline Analysis, Shubham Satish Nandanwar

Theses and Dissertations (Comprehensive)

Lake Huron is a popular tourist destination and is home to several businesses and residents. Since the shoreline is dynamic and is subject to change over the years due to several factors such as a change in water level, soil type, human encroachment, etc., these locations tend to encounter floods due to increased water levels and wind speed. This causes erosion and loss to the properties along the shoreline.

This study is based on two areas of interest named Pinery Provincial Park and Sauble Beach which are located on the shoreline of Lake Huron where Pinery Provincial Park is a ...

Estimating The Statistics Of Operational Loss Through The Analyzation Of A Time Series, Maurice L. Brown

Theses and Dissertations

In the world of finance, appropriately understanding risk is key to success or failure because it is a fundamental driver for institutional behavior. Here we focus on risk as it relates to the operations of financial institutions, namely operational risk. Quantifying operational risk begins with data in the form of a time series of realized losses, which can occur for a number of reasons, can vary over different time intervals, and can pose a challenge that is exacerbated by having to account for both frequency and severity of losses. We introduce a stochastic point process model for the frequency distribution ...

Predicting Power Using Time Series Analysis Of Power Generation And Consumption In Texas, Joshua Eysenbach, Bodie Franklin, Andrew J. Larsen, Joel Lindsey

SMU Data Science Review

Due to the recent power events in Texas, power forecasting has been brought national attention. Accurate demand forecasting is necessary to be sure that there is adequate power supply to meet consumer's needs. While Texas has a forecasting model created by the Electricity Reliability Council of Texas (ERCOT), constant efforts are required to ensure that the model stays at the state-of-the-art and is producing the most reliable forecasts possible. This research seeks to provide improved short- and medium-term forecasting models, bringing in state-of-the-art deep learning models to compare to ERCOT’s forecasts. A model that is more accurate than ...

Bayesian Variable Selection Strategies In Longitudinal Mixture Models And Categorical Regression Problems., Md Nazir Uddin

Electronic Theses and Dissertations

In this work, we seek to develop a variable screening and selection method for Bayesian mixture models with longitudinal data. To develop this method, we consider data from the Health and Retirement Survey (HRS) conducted by University of Michigan. Considering yearly out-of-pocket expenditures as the longitudinal response variable, we consider a Bayesian mixture model with $K$ components. The data consist of a large collection of demographic, financial, and health-related baseline characteristics, and we wish to find a subset of these that impact cluster membership. An initial mixture model without any cluster-level predictors is fit to the data through an MCMC ...

Characterizing The Northern Hemisphere Circumpolar Vortex Through Space And Time, Nazla Bushra

LSU Doctoral Dissertations

This hemispheric-scale, steering atmospheric circulation represented by the circumpolar vortices (CPVs) are the middle- and upper-tropospheric wind belts circumnavigating the poles. Variability in the CPV area, shape, and position are important topics in geoenvironmental sciences because of the many links to environmental features. However, a means of characterizing the CPV has remained elusive. The goal of this research is to (i) identify the Northern Hemisphere CPV (NHCPV) and its morphometric characteristics, (ii) understand the daily characteristics of NHCPV area and circularity over time, (iii) identify and analyze spatiotemporal variability in the NHCPV’s centroid, and (iv) analyze how CPV features ...

Time Series Forecasting Of Covid-19 Deaths In Massachusetts, Andrew Disher

Honors Program Theses and Projects

The aim of this study was to use data provided by the Department of Public Health in the state of Massachusetts on its online dashboard to produce a time series model to accurately forecast the number of new confirmed deaths that have resulted from the spread of CoViD-19. Multiple different time series models were created, which can be classified as either an Auto-Regressive Integrated Moving Average (ARIMA) model or a Regression Model with ARIMA Errors. Two ARIMA models were created to provide a baseline forecasting performance for comparison with the Regression Model with ARIMA Errors, which used the number of ...

Motor Control-Based Assessment Of Therapy Effects In Individuals Post-Stroke: Implications For Prediction Of Response And Subject-Specific Modifications, Ashley Rice

Doctoral Dissertations

Producing a coordinated motion such as walking is, at its root, the result of healthy communication pathways between the central nervous system and the musculoskeletal system. The central nervous system produces an electrical signal responsible for the excitation of a muscle, and the musculoskeletal system contains the necessary equipment for producing a movement-driving force to achieve a desired motion. Motor control refers to the ability an individual has to produce a desired motion, and the complexity of motor control is a mathematical concept stemming from how the electrical signals from the central nervous system translate to muscle activations. Exercising a ...

Unsupervised Multivariate Time Series Clustering, Md Monibor Rahman, Lasitha Vidyaratne, Alex Glandon, Khan Iftekharuddin

College of Engineering & Technology (Batten) Posters

Clustering is widely used in unsupervised machine learning to partition a given set of data into non-overlapping groups. Many real-world applications require processing more complex multivariate time series data characterized by more than one dependent variables. A few works in literature reported multivariate classification using Shapelet learning. However, the clustering of multivariate time series signals using Shapelet learning has not explored yet. Shapelet learning is a process of discovering those Shapelets which contain the most informative features of the time series signal. Discovering suitable Shapelets from many candidates Shapelet has been broadly studied for classification and clustering of univariate time ...

Institutional Context Drives Mobility: A Comprehensive Analysis Of Academic And Economic Factors That Influence International Student Enrollment At United States Higher Education Institutions, Natalie Cruz

College of Education & Professional Studies (Darden) Posters

International student enrollment (ISE) has become a hallmark of world-class higher education institutions (HEIs). Although the U.S. has welcomed the largest numbers of international students since the 1950s, ISE shrunk by 10% in the previous three years from an all-time high of 903,127 students in 2016/2017 (IIE, 2019). Research studies about international student mobility and enrollment highlights the significant role that academic and economic rationales play for international students. This quantitative, ex post facto study focused on the influence of ranking, tuition, Optional Practical Training, Gross Domestic Product, and the unemployment rate on ISE at 2,884 ...

Forecasting Of The Covid-19 Epidemic: A Scientometric Analysis, Pandri Ferdias, Ansari Saleh Ahmar

Library Philosophy and Practice (e-journal)

This study presented a scientometric analysis of scientific publications with discussions of forecasting and COVID-19. The data of this study were obtained from the Scopus database using the keywords: ( TITLE-ABS-KEY (forecast) AND TITLE-ABS-KEY (covid)) and the data were taken on March 26, 2021. This study was a scientometric study. The data were subsequently analyzed using the VosViewer and Bibliometrix R Package. The results showed that “COVID-19” was the keyword most frequently used by researchers, followed by “forecasting” and “human”. Authors who discussed the topic of forecasting COVID-19 come from 83 different countries/regions, with the most articles sent by authors ...

Demand Forecasting For Lucky Cement, Muhammad Arsalan Rashid

CBER Conference

As we know that demand is the Quantities of a good or service that people are ready to buy at various prices within some given time, other factors besides price held constant I tried to forecast the sales for next years. I removed seasonality factors and applied other determinants to predict the demand. By using values of independent variables in my Regression, the Annual Sales of Lucky Cement for period 2020-2021 is found to be around 7.9 Million Tons.

Regression Analyses Assessing The Impact Of Environmental Factors On Covid-19 Transmission And Mortality, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

No abstract provided.

Is Technological Progress A Random Walk? Examining Data From Space Travel, Michael Howell, Daniel Berleant, Hyacinthe Aboudja, Richard Segall, Peng-Hung Tsai

Journal of the Arkansas Academy of Science

Improvement in a variety of technologies can often be successful modeled using a general version of Moore’s law (i.e. exponential improvements over time). Another successful approach is Wright’s law, which models increases in technological capability as a function of an effort variable such as production. While these methods are useful, they do not provide prediction distributions, which would enable a better understanding of forecast quality

Farmer and Lafond (2016) developed a forecasting method which produces forecast distributions and is applicable to many kinds of technology. A fundamental assumption of their method is that technological progress can be ...

Writing At The Horizon: How Producing Imagined Narratives Affects Mood, David Yu-Zhong Liang

Senior Projects Fall 2021

The present study explores the effect of three different writing activities and their subsequent effects on participant mood. Writing has been of particular interest for psychologists due to its use in interventions aimed at working through traumatic or stressful periods, and recent research has begun to explore the use of narrative in placing traumatic events and experiences in greater context. However, purely therapeutic, intervention-based writing exercises exclude a large amount of more expressive, imagined creations and narratives, which may have the capacity to reorient, contextualize, and otherwise positively affect a person’s mood. This study investigates whether employing the imagination ...