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Longitudinal Data Analysis and Time Series Commons

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Human Capital At Home: Evidence From A Randomized Evaluation In The Philippines, Noam Angrist, Sarah Kabay, Dean S. Karlan, Lincoln Lau, Kevin M. Wong 2024 Pepperdine University

Human Capital At Home: Evidence From A Randomized Evaluation In The Philippines, Noam Angrist, Sarah Kabay, Dean S. Karlan, Lincoln Lau, Kevin M. Wong

Education Division Scholarship

Children spend most of their time at home in their early years, yet efforts to promote human capital at home in many low- and middle-income settings remain limited. We conduct a randomized controlled trial to evaluate an intervention which encourages parents and caregivers to foster human capital accumulation among their children between ages 3 and 5, with a focus on math and phonics skills. Children gain 0.52 and 0.51 standard deviations relative to the control group on math and phonics tests, respectively (p<0.001). A year later effects persist, but math gains dissipate to 0.15 (p=0.06) and phonics to 0.13 (p=0.12). Effects appear to be mediated largely through instructional support by parents and not other parent investment mechanisms, such as more positive parent-child interactions or additional time spent on education at home beyond the intervention. Our results show that parents can be effective conduits of educational instruction even in low-resource settings.


Gender-Specific Mental Health Outcomes In Central America: A Natural Experiment, Thea Nagasuru 2024 Ursinus College

Gender-Specific Mental Health Outcomes In Central America: A Natural Experiment, Thea Nagasuru

Computer Science Summer Fellows

While COVID lockdown measures have had varying effects on the mental health of different demographics, several bodies of research have noted their disparate effect on women. Why is women's mental health more negatively impacted by lockdown measures, and how much more are they impacted than men? How can we predict and mitigate these negative effects on women? This paper aims to contribute to answering those questions by comparing COVID stringency measures and their effect on the gap in depression rates between men and women in two neighboring countries: Nicaragua and Honduras.


The Application Of Elastic Distance In Astrophysical Time Series, Xiyang Zhang 2024 Western University

The Application Of Elastic Distance In Astrophysical Time Series, Xiyang Zhang

Electronic Thesis and Dissertation Repository

Elastic distances, e.g. dynamic time warping (DTW), evaluate the similarity between query and reference sequences by dynamic programming. The 1-Nearest-Neighbor predictor with DTW is one benchmark in time series classification. However, DTW is less efficient in astronomical time series because of ignorance of the information in time stamps and its dependence on the shape and magnitude between query and reference sequences. We apply two elastic distances which integrate the information in the time domain, time warp editing distance (TWED) and Skorohod distance, which is calculated by using Fre ́chet distance, to three astronomical datasets to compare with DTW and Euclidean …


A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte 2024 Southern Methodist University

A Symbolic Approach To Nonlinear Time Series Analysis, Ranjan Karki, Nibhrat Lohia, Michael B. Schulte

SMU Data Science Review

Current nonlinear time series methods such as neural networks forecast well. However, they act as a black box and are difficult to interpret, leaving the researchers and the audience with little insight into why the forecasts are the way they are. There is a need for a method that forecasts accurately while also being easy to interpret. This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression. The final method relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the …


Reevaluating Texas Energy Market Forecasts In The Wake Of Recent Extreme Weather Events, Robert A. Derner, Richard W. Butler II, Alexandria Neff, Adam R. Ruthford 2024 Southern Methodist University

Reevaluating Texas Energy Market Forecasts In The Wake Of Recent Extreme Weather Events, Robert A. Derner, Richard W. Butler Ii, Alexandria Neff, Adam R. Ruthford

SMU Data Science Review

This paper provides updated forecasts of energy demand in Texas and recognizes the impact of sustainable energy. It is important that the forecasts of the adoption of sustainable energy are reexamined after Winter Storm Uri crippled the Texas power grid and left many without power. This storm highlighted the issues the Texas power grid had and has continued to struggle with in supplying the state with energy. This paper will offer an overview of the relevant literature on the adoption of sustainable energy and relevant events that have occurred in the state of Texas that will give the reader the …


Modeling Human Temporal Eeg Responses To Vr Visual Stimuli, Richard R. Foster, Connor Delaney, Dean J. Krusienski, Cheng Ly 2024 Virginia Commonwealth University

Modeling Human Temporal Eeg Responses To Vr Visual Stimuli, Richard R. Foster, Connor Delaney, Dean J. Krusienski, Cheng Ly

Biology and Medicine Through Mathematics Conference

No abstract provided.


Assessing Reproducibility Of Brain-Behavior Associations Using Bootstrap Aggregation Methods, ZHETAO CHEN 2024 Washington University in St. Louis

Assessing Reproducibility Of Brain-Behavior Associations Using Bootstrap Aggregation Methods, Zhetao Chen

Arts & Sciences Electronic Theses and Dissertations

在本论文中,随着越来越多地利用静息态功能连接 MRI (rs-fcMRI) 将神经活动与病理状况联系起来,我们面临着对此类数据可靠性的普遍担忧。我们的探索集中于提高人类连接组计划(HCP)数据集框架内大脑行为关联的可重复性。我们采用两种不同的引导聚合方法来研究功能连接可靠性的增强:使用循环块引导(CBB)的单独时间序列装袋和使用线性支持向量回归(LSVR)模型的主题级装袋。我们对 CBB 个体时间序列 bagging 的调查表明,这种方法并不能显着增强大脑行为关联的可重复性。这一发现指出了实现可靠的功能连接措施的复杂性以及某些聚合方法在克服这一挑战方面的局限性。相比之下,我们的学科水平考试 通过 LSVR 模型装袋呈现出更有希望的结果。这种方法显着增强了分析之间模型权重的可靠性,证明了其在提高数据稳健性和可重复性方面的功效。两种方法的这种不同影响强调了适当的分析策略在提高神经影像数据可靠性方面的关键作用。通过描述这两种方法的结果,本论文有助于对神经影像领域的数据可靠性进行更广泛的讨论。它强调了在不同数据集上持续进行方法创新和验证的必要性,以提高 rs-fcMRI 研究的可靠性和可解释性。


The Performance Of Arima And Arfima In Modelling The Exchange Rate Of Nigeria Currency To Other Currencies, Adewole Ayoade I. 2024 Department of Mathematics, Tai Solarin University of Education Ijagun Ogun State Nigeria.

The Performance Of Arima And Arfima In Modelling The Exchange Rate Of Nigeria Currency To Other Currencies, Adewole Ayoade I.

Al-Bahir Journal for Engineering and Pure Sciences

Economic performance of a nation depends majorly on the stability of foreign exchange rate; the economic viability hangs on the exchange rate of local currencies against other currencies across the globe. Box – Jenkins Approach was employed to model the Naira exchange rate to other major currencies using Autoregressive Integrated Moving Average (ARIMA) and The autoregressive fractional integral moving average (ARFIMA) models. This studies aimed on measuring forecast ability of Autoregressive Integrated Moving Average (ARIMA) (p,d,q) and autoregressive fractional integral moving average (ARFIMA) (p, fd, q) models for stationary type series that exhibit features of Long memory properties. Results indicate …


Deterministic Global 3d Fractal Cloud Model For Synthetic Scene Generation, Aaron M. Schinder, Shannon R. Young, Bryan J. Steward, Michael L. Dexter, Andrew Kondrath, Stephen Hinton, Ricardo Davila 2024 Air Force Institute of Technology

Deterministic Global 3d Fractal Cloud Model For Synthetic Scene Generation, Aaron M. Schinder, Shannon R. Young, Bryan J. Steward, Michael L. Dexter, Andrew Kondrath, Stephen Hinton, Ricardo Davila

Faculty Publications

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to …


Machine Learning And Geostatistical Approaches For Discovery Of Weather And Climate Events Related To El Niño Phenomena, Sachi Perera 2024 Chapman University

Machine Learning And Geostatistical Approaches For Discovery Of Weather And Climate Events Related To El Niño Phenomena, Sachi Perera

Computational and Data Sciences (PhD) Dissertations

El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where temperature and precipitation are influenced across the globe, causing droughts and floods simultaneously. In this dissertation, we first derived a drought vulnerability index for the Nile basin, identifying regions with high and low drought risk under ENSO conditions. Next, we evaluated the coherence and periodicity of the ENSO signal to detect its implications on MENA …


A Novel Correction For The Multivariate Ljung-Box Test, Minhao Huang 2024 Chapman University

A Novel Correction For The Multivariate Ljung-Box Test, Minhao Huang

Computational and Data Sciences (PhD) Dissertations

This research introduces an analytical improvement to the Multivariate Ljung-Box test that addresses significant deviations of the original test from the nominal Type I error rates under almost all scenarios. Prior attempts to mitigate this issue have been directed at modification of the test statistics or correction of the test distribution to achieve precise results in finite samples. In previous studies, focused on designing corrections to the univariate Ljung-Box, a method that specifically adjusts the test rejection region has been the most successful of attaining the best Type I error rates. We adopt the same approach for the more complex, …


Representation Learning For Generative Models With Applications To Healthcare, Astronautics, And Aviation, Van Minh Nguyen 2024 Florida Institute of Technology

Representation Learning For Generative Models With Applications To Healthcare, Astronautics, And Aviation, Van Minh Nguyen

Theses and Dissertations

This dissertation explores applications of representation learning and generative models to challenges in healthcare, astronautics, and aviation.

The first part investigates the use of Generative Adversarial Networks (GANs) to synthesize realistic electronic health record (EHR) data. An initial attempt at training a GAN on the MIMIC-IV dataset encountered stability and convergence issues, motivating a deeper study of 1-Lipschitz regularization techniques for Auxiliary Classifier GANs (AC-GANs). An extensive ablation study on the CIFAR-10 dataset found that Spectral Normalization is key for AC-GAN stability and performance, while Weight Clipping fails to converge without Spectral Normalization. Analysis of the training dynamics provided further …


Efficient Fully Bayesian Approaches To Brain Activity Mapping With Complex-Valued Fmri Data: Analysis Of Real And Imaginary Components In A Cartesian Model And Extension To Magnitude And Phase In A Polar Model, Zhengxin Wang 2024 Clemson University

Efficient Fully Bayesian Approaches To Brain Activity Mapping With Complex-Valued Fmri Data: Analysis Of Real And Imaginary Components In A Cartesian Model And Extension To Magnitude And Phase In A Polar Model, Zhengxin Wang

All Dissertations

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data, and therefore, lead to underutilization of available data and flawed statistical assumptions. This dissertation proposes two efficient, fully Bayesian approaches for the analysis of complex-valued functional magnetic resonance imaging (cv-fMRI) time series.

Chapter 2 introduces the model, referred to as CV-sSGLMM, using the real and imaginary components of cv-fMRI data and sparse spatial generalized linear mixed model prior. This model extends the …


Lstm-Based Recurrent Neural Network Predicts Influenza-Like-Illness In Variable Climate Zones, Alfred Amendolara, Christopher Gowans, Joshua Barton, David Sant, Andrew Payne 2024 Noorda College of Osteopathic Medicine

Lstm-Based Recurrent Neural Network Predicts Influenza-Like-Illness In Variable Climate Zones, Alfred Amendolara, Christopher Gowans, Joshua Barton, David Sant, Andrew Payne

Annual Research Symposium

Purpose: Influenza virus is responsible for a recurrent, yearly epidemic in most temperate regions of the world. For the 2021-2022 season the CDC reports 5000 deaths and 100,000 hospitalizations, a significant number despite the confounding presence of SARS-CoV-2. The mechanisms behind seasonal variance in flu burden are not well understood. Based on a previously validated model, this study seeks to expand understanding of the impact of variable climate regions on seasonal flu trends. To that end, three climate regions have been selected. Each region represents a different ecological region and provides different weather patterns showing how the climate variables impact …


Outpatient Fall Prevention In Ambulatory Adults 65 Years Old And Over, Dorothy L. Osborne-White 2024 University of Texas at Arlington

Outpatient Fall Prevention In Ambulatory Adults 65 Years Old And Over, Dorothy L. Osborne-White

Doctor of Nursing Practice (DNP) Scholarly Projects

Abstract

Background: In the United States (U.S.), falls are the leading cause of injury among adults 65 and over, resulting in 36 million falls yearly (Moreland et al., 2020). According to the Centers for Disease Control and Prevention (CDC, 2023), one in four older adults experiences a fall each year. Falls are the world's second most prominent cause of accidental deaths (World Health Organization [WHO], 2021). Falls are the leading cause of both fatal and non-fatal injuries among older adults (Moreland et al., 2020).

Methods: A quality improvement project that included a fall bundle was implemented in a primary clinic. …


Sparse Representation Learning For Temporal Networks, Maxwell McNeil 2024 University at Albany, State University of New York

Sparse Representation Learning For Temporal Networks, Maxwell Mcneil

Electronic Theses & Dissertations (2024 - present)

Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …


Performing Holt-Winters Time Series Forecasting Using Neural Network Based Models, Kazeem Olanrewaju Bankole 2024 Georgia Southern University

Performing Holt-Winters Time Series Forecasting Using Neural Network Based Models, Kazeem Olanrewaju Bankole

Electronic Theses and Dissertations

We show how to create Artificial Neural Network based models for performing the well- known Holt-Winters time series analysis. Our work fares well compared to the well-known Holt-Winter time series prediction method while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models


Differential Impacts Of Weather Anomalies On Household Energy Expenditure Shares: A Comparison Of Clustered Panel Analysis Methods, Jordan Champion 2024 University of Kentucky

Differential Impacts Of Weather Anomalies On Household Energy Expenditure Shares: A Comparison Of Clustered Panel Analysis Methods, Jordan Champion

Theses and Dissertations--Agricultural Economics

Recent emphasis on environmental justice has highlighted deficiencies in our energy system that produce disparities in accessibility and affordability for the most vulnerable. Meanwhile, the realities of a gradually warming climate and the onset of a global energy crisis (IEA 2022) have coincidently contributed to spikes in both energy prices and demand. These implications threaten to further exacerbate existing disparities for income-constrained and vulnerable populations, enhancing their risk of falling into prolonged insecurity. To ensure our transition to a just, sustainable future, we must first ensure equitable access to affordable and reliable energy for everyone. Combining household-level panel and state-level …


Self-Exciting Point Processes In Real Estate, Ian Fraser 2024 Wilfrid Laurier University

Self-Exciting Point Processes In Real Estate, Ian Fraser

Theses and Dissertations (Comprehensive)

This thesis introduces a novel approach to analyzing residential property sales through the lens of stochastic processes by employing point processes. Herein, property sales are treated as point patterns, using self-exciting point process models and a variety of statistical tools to uncover underlying patterns in the data. Key findings include the identification and explanation of clustering in both space and time, and the efficacy of a temporal Hawkes process with a sinusoidal background in predicting home sale occurrences. The temporal analysis starts by employing the state of art techniques for time series data like regression, autoregressive, and autoregressive integrated moving …


Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia 2023 Brigham Young University

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …


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