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Longitudinal Data Analysis and Time Series Commons™

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2019

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Articles 1 - 22 of 22

Full-Text Articles in Longitudinal Data Analysis and Time Series

Seasonal Time Series Models With Application To Weather And Lake Level Data, Mengqing Qin Dec 2019

Seasonal Time Series Models With Application To Weather And Lake Level Data, Mengqing Qin

MSU Graduate Theses

This work studies seasonal time series models with application to lake level and weather data. The thesis includes related time series concepts, integrated autoregressive moving average models (abbreviated as ARIMA), parameter estimation, model diagnostics, and forecasting. The studied time series models are applied to the data of daily lake level in Beaver Lake (1988-2017) and the data of daily maximum temperature in New York Central Park (1870-2017). Due to seasonality of the data, three different approaches are proposed to the modeling: regression method, functional ARIMA method and multiplicative seasonal ARIMA method. The forecasted values of the year 2018 are compared …

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Predicting Wind Turbine Blade Erosion Using Machine Learning, Casey Martinez, Festus Asare Yeboah, Scott Herford, Matt Brzezinski, Viswanath Puttagunta Aug 2019

Predicting Wind Turbine Blade Erosion Using Machine Learning, Casey Martinez, Festus Asare Yeboah, Scott Herford, Matt Brzezinski, Viswanath Puttagunta

SMU Data Science Review

Using time-series data and turbine blade inspection assessments, we present a classification model in order to predict remaining turbine blade life in wind turbines. Capturing the kinetic energy of wind requires complex mechanical systems, which require sophisticated maintenance and planning strategies. There are many traditional approaches to monitoring the internal gearbox and generator, but the condition of turbine blades can be difficult to measure and access. Accurate and cost- effective estimates of turbine blade life cycles will drive optimal investments in repairs and improve overall performance. These measures will drive down costs as well as provide cheap and clean electricity …

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Longitudinal Analysis With Modes Of Operation For Aes, Dana Geislinger, Cory Thigpen, Daniel W. Engels Aug 2019

Longitudinal Analysis With Modes Of Operation For Aes, Dana Geislinger, Cory Thigpen, Daniel W. Engels

SMU Data Science Review

In this paper, we present an empirical evaluation of the randomness of the ciphertext blocks generated by the Advanced Encryption Standard (AES) cipher in Counter (CTR) mode and in Cipher Block Chaining (CBC) mode. Vulnerabilities have been found in the AES cipher that may lead to a reduction in the randomness of the generated ciphertext blocks that can result in a practical attack on the cipher. We evaluate the randomness of the AES ciphertext using the standard key length and NIST randomness tests. We evaluate the randomness through a longitudinal analysis on 200 billion ciphertext blocks using logistic regression and …

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Sample Size Calculation Of Clinical Trials With Correlated Outcomes, Dateng Li Aug 2019

Sample Size Calculation Of Clinical Trials With Correlated Outcomes, Dateng Li

Statistical Science Theses and Dissertations

In this thesis, we investigate sample size calculation for three kinds of clinical trials: (1). Randomized controlled trials (RCTs) with longitudinal count outcomes; (2). Cluster randomized trials (CRTs) with count outcomes; (3). CRTs with multiple binary co-primary endpoints.

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Estimation Of Association Between A Longitudinal Marker And Interval-Censored Progression Times, Naghmeh Daneshi Jul 2019

Estimation Of Association Between A Longitudinal Marker And Interval-Censored Progression Times, Naghmeh Daneshi

Dissertations and Theses

In longitudinal studies, we observe the subjects who are likely to progress to a new state during the study time. For example, in clinical trials the stage of a progressing disease is recorded at each follow-up visit. The primary goal is to estimate the relationship between the attributes and the subject's progression state. In such studies, some subjects complete all their follow-up visits and their progression state are observed without any missingness. However, others miss their follow-up visits and when they come back, they learn that they have progressed to a new state. In this case, not only are their …

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Copula-Based Zero-Inflated Count Time Series Models, Mohammed Sulaiman Alqawba Jul 2019

Copula-Based Zero-Inflated Count Time Series Models, Mohammed Sulaiman Alqawba

Mathematics & Statistics Theses & Dissertations

Count time series data are observed in several applied disciplines such as in environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, say zero, may occur more often than usual. Additionally, serial dependence might be found among these counts if they are recorded over time. Overlooking the frequent occurrence of zeros and the serial dependence could lead to false inference. In this dissertation, we propose two classes of copula-based time series models for zero-inflated counts with the presence of covariates. Zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and zero-inflated Conway-Maxwell-Poisson (ZICMP) distributed marginals of the …

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Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga Jun 2019

Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga

LSU Master's Theses

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different …

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Bias Reduction In Machine Learning Classifiers For Spatiotemporal Analysis Of Coral Reefs Using Remote Sensing Images, Justin J. Gapper May 2019

Bias Reduction In Machine Learning Classifiers For Spatiotemporal Analysis Of Coral Reefs Using Remote Sensing Images, Justin J. Gapper

Computational and Data Sciences (PhD) Dissertations

This dissertation is an evaluation of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean 2) Coral Reef Change Detection in Remote Pacific Islands using Support Vector Machine Classifiers 3) A Generalized Machine Learning Classifier for Spatiotemporal Analysis of Coral Reefs in the Red Sea. The aim of this dissertation is to propose and evaluate a …

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Demand Forecasting: An Open-Source Approach, Murtada Shubbar, Jared Smith May 2019

Demand Forecasting: An Open-Source Approach, Murtada Shubbar, Jared Smith

SMU Data Science Review

In this paper, we compare demand forecasting methods used by the supply chain department at Bilports to open-source forecasting methods. The design and implementation of the open-source forecasting system also attempts to use several external datasets such as consumer sentiment, housing permit starts, and weather to improve prediction quality. Additionally, the performance of the forecast is evaluated by the reduction of shipment lead times from China, the company’s primary vendor. The objective of our paper is to improve Bilports’s forecasting capabilities. The primary motivation of this paper is to increase forecasting accuracy and identify the weaknesses of the methods used …

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Feasibility Of Multi-Year Forecast For The Colorado River Water Supply: Time Series Modeling, Brian Plucinski May 2019

Feasibility Of Multi-Year Forecast For The Colorado River Water Supply: Time Series Modeling, Brian Plucinski

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

The Colorado River is one of the largest resources for water in the United States, as well as being an important asset to the economy. Previous studies have shown a connection between the Great Salt Lake and the Colorado River. This study used time series analysis to build models to predict the water supply of the Colorado River ten years out. These models used data from the Colorado River in addition to Great Salt Lake water elevation. Several models suggest a decline in water supply from 2013 – 2020, before starting to increase. These predictions differ from predictions published by …

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Sampling Studies For Longitudinal Functional Data, Toni Jassel May 2019

Sampling Studies For Longitudinal Functional Data, Toni Jassel

Theses, Dissertations and Culminating Projects

We study the data setting consisting of functional data sets repeatedly observed over time. The focus is on the dynamic prediction of the future trajectory for a subject. Regression methods based on dynamic functional models are used for dynamic prediction of individual trajectories. We propose strategies for the selection of the study sampling design in the context of longitudinal functional data. An application to simulated child growth data is presented. The height-for-age z-score (HAZ) was the response variable in the functional dynamic models for prediction. The intent was to recommend four months for removal in our initial historic data set. …

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A Bayesian Framework For Estimating Seismic Wave Arrival Time, Hua Zhong May 2019

A Bayesian Framework For Estimating Seismic Wave Arrival Time, Hua Zhong

Graduate Theses and Dissertations

Because earthquakes have a large impact on human society, statistical methods for better studying earthquakes are required. One characteristic of earthquakes is the arrival time of seismic waves at a seismic signal sensor. Once we can estimate the earthquake arrival time accurately, the earthquake location can be triangulated, and assistance can be sent to that area correctly. This study presents a Bayesian framework to predict the arrival time of seismic waves with associated uncertainty. We use a change point framework to model the different conditions before and after the seismic wave arrives. To evaluate the performance of the model, we …

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Rates Of Relative Sea Level Rise Along The United States East Coast, Jesse N. Beckman, Joseph E. Garcia Apr 2019

Rates Of Relative Sea Level Rise Along The United States East Coast, Jesse N. Beckman, Joseph E. Garcia

Virginia Journal of Science

Recent studies have indicated that some coastal areas, including the East Coast of the United States, are experiencing higher rates of sea level rise than the global average. Rates of relative sea level rise are affected by changes in ocean dynamics, as well as by surface elevation fluctuations due to local land subsidence or uplift. In this study, we derived long-term trends in annual mean relative sea level using tide gauge data obtained from the Permanent Service for Mean Sea Level for stations along the United States East Coast. Stations were grouped by location into the Northeast, Mid-Atlantic, and Southeast …

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Feature Selection For Longitudinal Data By Using Sign Averages To Summarize Gene Expression Values Over Time, Suyan Tian, Chi Wang Mar 2019

Feature Selection For Longitudinal Data By Using Sign Averages To Summarize Gene Expression Values Over Time, Suyan Tian, Chi Wang

Biostatistics Faculty Publications

With the rapid evolution of high-throughput technologies, time series/longitudinal high-throughput experiments have become possible and affordable. However, the development of statistical methods dealing with gene expression profiles across time points has not kept up with the explosion of such data. The feature selection process is of critical importance for longitudinal microarray data. In this study, we proposed aggregating a gene’s expression values across time into a single value using the sign average method, thereby degrading a longitudinal feature selection process into a classic one. Regularized logistic regression models with pseudogenes (i.e., the sign average of genes across time as predictors) …

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Spatiotemporal Dynamics Of Nitrogen And Carbon Biogeochemistry In A Wetland-Stream Sequence, Patrick E. Hurley Jan 2019

Spatiotemporal Dynamics Of Nitrogen And Carbon Biogeochemistry In A Wetland-Stream Sequence, Patrick E. Hurley

Graduate Student Theses, Dissertations, & Professional Papers

Studies of aquatic ecosystems often segregate streams from the influential ponds, lakes, and wetland zones that act as important transitions between terrestrial and fluvial systems. Across the aquatic landscape, these zones interact to form linked ecosystems that function as discrete nutrient processing domains, shifting biogeochemical signals due to spatial and temporal variability in hydrologic and biologic controls. Using a mass-balance approach, we profiled nutrient dynamics along a 23-km wetland-stream sequence over three seasons. Hydrologic, morphologic, and biologic conditions, as well as landscape attributes, were quantified to determine potential controls on biogeochemical cycling in a tributary of the Upper Clark Fork …

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Statistical Methods For Mixed Frequency Data Sampling Models, Yun Liu Jan 2019

Statistical Methods For Mixed Frequency Data Sampling Models, Yun Liu

Dissertations, Master's Theses and Master's Reports

The MIDAS models are developed to handle different sampling frequencies in one regression model, preserving information in the higher sampling frequency. Time averaging has been the traditional parametric approach to handle mixed sampling frequencies. However, it ignores information potentially embedded in high frequency. MIDAS regression models provide a concise way to utilize additional information in HF variables. While a parametric MIDAS model provides a parsimonious way to summarize information in HF data, nonparametric models would maintain more flexibility at the expense of the computational complexity. Moreover, one parametric form may not necessarily be appropriate for all cross-sectional subjects. This thesis …

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On Cluster Robust Models, José Bayoán Santiago Calderón Jan 2019

On Cluster Robust Models, José Bayoán Santiago Calderón

CGU Theses & Dissertations

Cluster robust models are a kind of statistical models that attempt to estimate parameters considering potential heterogeneity in treatment effects. Absent heterogeneity in treatment effects, the partial and average treatment effect are the same. When heterogeneity in treatment effects occurs, the average treatment effect is a function of the various partial treatment effects and the composition of the population of interest. The first chapter explores the performance of common estimators as a function of the presence of heterogeneity in treatment effects and other characteristics that may influence their performance for estimating average treatment effects. The second chapter examines various approaches …

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Toward Using High-Frequency Coastal Radars For Calibration Of S-Ais Based Ocean Vessel Tracking Models, Ben Freidrich Jan 2019

Toward Using High-Frequency Coastal Radars For Calibration Of S-Ais Based Ocean Vessel Tracking Models, Ben Freidrich

Theses and Dissertations (Comprehensive)

Most of the world relies on ships for transportation, shipping, and tourism. Automatic Identification System messages are transmitted from ships and provide a wealth of positional data on these open ocean vessels. This data is being utilized to determine the optimal path for ships, as well as predicting where a ship may be going in the near future. It has only been in the past decade that Automatic Identification Systems (AIS) signals have been easily received with satellites (S-AIS) so there have been few studies that look at using available information and pairing it with the new abundance of ship …

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Variable Selection In Accelerated Failure Time (Aft) Frailty Models: An Application Of Penalized Quasi-Likelihood, Sarbesh R. Pandeya Jan 2019

Variable Selection In Accelerated Failure Time (Aft) Frailty Models: An Application Of Penalized Quasi-Likelihood, Sarbesh R. Pandeya

Electronic Theses and Dissertations

Variable selection is one of the standard ways of selecting models in large scale datasets. It has applications in many fields of research study, especially in large multi-center clinical trials. One of the prominent methods in variable selection is the penalized likelihood, which is both consistent and efficient. However, the penalized selection is significantly challenging under the influence of random (frailty) covariates. It is even more complicated when there is involvement of censoring as it may not have a closed-form solution for the marginal log-likelihood. Therefore, we applied the penalized quasi-likelihood (PQL) approach that approximates the solution for such a …

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Bayesian Analysis For The Intraclass Model And For The Quantile Semiparametric Mixed-Effects Double Regression Models, Duo Zhang Jan 2019

Bayesian Analysis For The Intraclass Model And For The Quantile Semiparametric Mixed-Effects Double Regression Models, Duo Zhang

Dissertations, Master's Theses and Master's Reports

This dissertation consists of three distinct but related research projects. The first two projects focus on objective Bayesian hypothesis testing and estimation for the intraclass correlation coefficient in linear models. The third project deals with Bayesian quantile inference for the semiparametric mixed-effects double regression models. In the first project, we derive the Bayes factors based on the divergence-based priors for testing the intraclass correlation coefficient (ICC). The hypothesis testing of the ICC is used to test the uncorrelatedness in multilevel modeling, and it has not well been studied from an objective Bayesian perspective. Simulation results show that the two sorts …

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Global Warming Statistical Analysis, Jared Skinner Jan 2019

Global Warming Statistical Analysis, Jared Skinner

Williams Honors College, Honors Research Projects

This paper will investigate global warming and its effects on natural disasters. I will review the historic movements of climate change and activism, as well as the current discussions surrounding global warming. Secondly, I will examine various datasets, paying attention to the severity and frequency of specific natural disasters. I will then touch briefly on the topic of catastrophe modeling as it relates to the increased risk and losses associated with the discussed natural disasters and how those put the problem of global warming in a framework which financial and government institutions can grasp. I will also be analyzing economic …

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Bayesian Nonparametric Analysis Of Longitudinal Data With Non-Ignorable Non-Monotone Missingness, Yu Cao Jan 2019

Bayesian Nonparametric Analysis Of Longitudinal Data With Non-Ignorable Non-Monotone Missingness, Yu Cao

Theses and Dissertations

In longitudinal studies, outcomes are measured repeatedly over time, but in reality clinical studies are full of missing data points of monotone and non-monotone nature. Often this missingness is related to the unobserved data so that it is non-ignorable. In such context, pattern-mixture model (PMM) is one popular tool to analyze the joint distribution of outcome and missingness patterns. Then the unobserved outcomes are imputed using the distribution of observed outcomes, conditioned on missing patterns. However, the existing methods suffer from model identification issues if data is sparse in specific missing patterns, which is very likely to happen with a …

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