Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 21 for “"Least Absolute Shrinkage and Selection Operator (LASSO)"”.
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Variable selection in discrete survival models
Selection of variables is vital in high dimensional statistical modelling as it aims to identify the right subset model. However, variable selection for discrete survival analysis poses many challenges due to a complicated data structure. Survival data might have unobserved heterogeneity leading to …
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Empirical rate-distortion study of compressive sensing-based joint source-channel coding
… rate-distortion behavior of both point-to-point and distributed cases. First, we propose an efficient algorithm to find the 4-norm regularization parameter that is required by the Least Absolute Shrinkage and Selection Operator (LASSO) which we use as a CS decoder. We then show that, for a …
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Bayesian generalized additive model selection
… models (GAMs) offer a parsimonious, flexible and interpretable framework for regression, particularly when handling a large numbers of candidate predictors. This thesis addresses the GAM variable selection problem: categorizing each candidate predictor's effect type to be linear, non-linear or …
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Modeling Areal Measures of Campsite Impacts on the Appalachian National Scenic Trail, USA Using Airborne LiDAR and Field Collected Data
… of any long-distance trail due to the many land types and management agencies involved. Large proportions of long-distance trails have at-large camping policies, resulting in camping problems associated with visitor-chosen or developed campsites. Several long-term monitoring studies in areas …
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Crop Water Deficits and their Season-ahead Climate Determinants Across the United States
… in the United States provides an essential food and revenue source and it faces severe challenges due to increasing climate variability, which reduces reliable access to water needed for the crops. Unreliable access to water and the potential for drought result in major economic losses to the …
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Spatial Infectious Disease Transmission Models: Variable Screening Methods and Logistic Formulation.
… affecting not only health, economies, and agriculture, but also global trade, social structures, and education (Rohr et al., 2019; Vurro et al., 2010; Anderson, 2002). Advanced mathematical models of infectious diseases, particularly individual-level models (ILMs), play a crucial role …
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The Intergenerational Transmission Effect of Depression: Causality, Resilience & Decomposition
… parent also suffers from poor mental health, and this intergenerational transmission effect of depression is large and highly significant. Previous work has attempted to investigate whether it is nurture or nature effects which drive this large transmission effect. While this work has found …
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The Seasonality and Climate Predictability of the Frequency of Extreme Flood Events Across the Contiguous United States
… flood events across the United States (US) and outlines the impact of large-scale climate on its spatio-temporal variability. The analysis was conducted on 317 stream gages from the United States Geological Survey (USGS) Hydro-Climatic Data Network (HDCN) over a period of 68 years …
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Machine learning models for functional impairment risk prediction in ischemic stroke patients
… predictive accuracy of machine-learning models and regression-based models using computer simulations. Methods: Using data from the Precise and Rapid Assessment of Collaterals with Multi-phase CT Angiography (PROVE-IT). The Modified Rankin Scale (mRS) score was used to assess the 90-day …
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Essays on Corporate Default Prediction
… interest in academic research, business practice and government regulation. The recent financial crisis, during which unexpected corporate insolvencies had caused severe damage to the aggregate economy, highlights the crucial importance of an accurate corporate default prediction. Consequently, …
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A Citizen-Science Approach for Urban Flood Risk Analysis Using Data Science and Machine Learning
… impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches …
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Moving towards an evidence informed approach to talent identification and development in Scottish soccer long term athlete development pathways.
… aim to optimise the identification of talent and the development of players to create a resource as first team players or sellable assets. Since the turn of the millennium, the literature that examines the impact of talent ID and development practices has predominantly been cross-sectional in …
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Volatility and return forecasting : time series and options-based methods
This thesis attempts to model and forecast returns and realized volatility using two different methods: time series models that exploit the historical information set and options-based approach that provides a natural forecast of return variation from listed option prices. Both univariate and …
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Machine Learning and Additive Manufacturing Based Antenna Design Techniques
… tackling the universal antenna design challenges and achieving automated antenna design for a broad range of applications. First, we investigate the implementation and accuracy of few modern machine learning techniques including, least absolute shrinkage and selection operator (lasso), artificial …
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Transcriptomic predictors of outcomes in acute infection
… to infection, lead to high patient morbidity and mortality. Conditions that mimic acute infections and sepsis also lead to delays in appropriate patient care and adverse outcomes given the low threshold for suspicion and initial treatment of infection in the emergency department (ED). …
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New progress in hot-spots detection, partial-differential-equation-based model identification and statistical computation
… (PDE-based) model identification, and (3) optimization in the Least Absolute Shrinkage and Selection Operator (Lasso) type problem. In this thesis, we have four main works. Chapter 1 and Chapter 2 fall in the first area, i.e., hot-spots detection in spatio-temporal data. Chapter 3 …
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Optimizing Adherence to Antiretroviral Therapy among Adolescents Living with HIV in Low and Middle-Income Settings
… of mother to child transmission (PMTCT) and pre-exposure prophylaxis—new infections continue to occur. For example, in 2021 alone, the number of new HIV infections among adolescents aged 10 to 19 was 160,000 cases. Moreover, the rate of HIV-related deaths among adolescents has only …
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The Use of Oncology Electronic Health Record Databases to Assess the Effectiveness of Breast Cancer Treatment
… in clinical oncology that pertain to the safety and effectiveness of medications. They complement randomized trials by including frail and complex patients seen in routine care that reflect real-world practice patterns and treatment adherence. Historically, pharmacoepidemiology research in the …
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Application of data-driven technologies for asthma self-management
… affecting around 5.4 million people in the UK and more than 300 million people worldwide. Every 10 seconds in the UK, someone has an asthma attack. Some of these attacks are life-threatening with over 1,400 annual deaths estimated in the UK. Since there is no known cure for asthma, …
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