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 24 for “"LASSO regression"”.
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A Comparison of Logistic, RIDGE, and LASSO Regression with Heart Failure Risk Data: Effects of Sample Size, Predictor Correlation, and Predictor Weight on Outcome Accuracy
<p>Logistic Regression (LR), LASSO regression, and RIDGE regression are standard classification techniques for predicting a dichotomous output. Since these methods are applied for similar purposes and have different features, it is crucial to evaluate the performance of these methods under …
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Penalized Regression Methods with Application to Generalized Linear Models, Generalized Additive Models, and Smoothing
Recently, penalized regression has been used for dealing problems which found in maximum likelihood estimation such as correlated parameters and a large number of predictors. The main issues in this regression is how to select the optimal model. In this thesis, Schall’s algorithm is proposed as an …
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The Use of Regularization to Detect Racial Inequities in Pay Equity Studies: An Empirical Study and Reflections on Regulation Methods
<p>Since the late 1970s, multiple linear regression has been the preferred method for identifying discrimination in pay. An empirical study on this topic was conducted using quantitative critical methods. A literature review first examined conflicting views on using multiple linear regression in …
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High-Throughput Phenotyping and Genomic Prediction in Multi-Environment Plant Breeding Field Trials
… features were extracted at each timepoint. LASSO regression models trained on image feature sets were able to predict days to heading (mean R2 = 0.76), days to maturity (mean R2 = 0.84), plant height (mean R2 = 0.70), and grain yield (mean R2 = 0.64) within testing environments more …
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Basis Risk in Variable Annuities
… assess different variable selection models. The LASSO regression is shown to be most effective at identifying the most suitable (combination of) mapping instruments that minimize basis risk, compared to other test-based and screening-based models. I supplement it with the Sure Independence …
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Anomalijų aptikimas finansų rinkose /
… from 1990 to 2019. LSTM neural networks and LASSO regression were used for data modeling. In the analyzed situation, the algorithm using Long Short Term Memory neural networks showed better results than the algorithm using the Least Absolute Shrinkage and Selection Operator regression, …
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Anomaly detection methods for detecting cyber attacks in industrial control systems
… method is implemented using residuals from LASSO regression models. The thesis also develops an ensemble method which uses an optimization formulation to combine the output of multiple models in a way that minimizes detection delay. When evaluated on 380 samples from the Kasperskey Tennessee …
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Genetics of serum urate regulation in human health and disease
… 191 lipid species. Using partial correlation and lasso regression, I have identified and replicated 11 protein biomarkers whose serum levels covary with urate independently of the other biomarkers. The associated proteins are involved in diverse processes including phosphate metabolism and bone …
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Optimizing deep learning networks using multi-armed bandits
… trees, SVM, Naïve Bayes, LDA, QDA, logistic regression, Gaussian process classifier, kernel ridge regression, LASSO regression, linear regression, Bayesian Ridge regression, boosting, bagging and random forests. The results on the data sets show that some of the new methods (i) generalize …
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Economics Meets Mortality
… and employs a two-stage least squares (2SLS) regression framework with mass layoffs as an instrument to identify the causal effect of unemployment on mortality. Results indicate that unemployment leads to nine additional deaths per 100,000 people, with pronounced effects among older men and in …
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Diversity shrinkage of pareto-optimal solutions in hiring practice: Simulation, shrinkage formula, and a regularization technique
… and regularization (similar to ridge regression, LASSO regression, or elastic nets; in the context of Pareto-optimal weighting with two criteria). An R package is developed to estimate Pareto-optimal solutions in personnel selection (i.e., ParetoR package), which includes: (a) De Corte …
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Contributions to Data Reduction and Statistical Model of Data with Complex Structures
… of features. Traditional methods, such as linear regression or LASSO regression, cannot effectively deal with such a large dataset directly. This dissertation aims to develop several techniques to effectively analyze large datasets with complex structures in the observational, experimental and …
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Recognizing cardiovascular disease patterns with machine learning using NHANES accelerometer determined physical activity data
… on prior knowledge of the data. In general, the lasso regression, support vector machines (SVM) and random forest (RF) classifiers all performed well on large sets of data-driven features, achieving greater than 82% classification accuracy when time spent in PA intensity categories was combined …
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Development of two disease specific scales to assess the impact of severe asthma on patients' lives
… shortened using a statistical method (LASSO regression) that identified items relevant to the aetiology of severe asthma (N=100). This resulted in the GSQ-A. The SAQ and GSQ-A are valid measures of HRQoL and extra-pulmonary symptom burden respectively. Both burdens are often …
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A Citizen-Science Approach for Urban Flood Risk Analysis Using Data Science and Machine Learning
… Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with an embedded Zero-Inflation (ZI) model, the variables statistically significant as predictors, specific to each zip code, are detected. Second, with an intent to understand how factors affect the spatial variability …
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Prediction of isobaric heat capacities of room temperature ionic liquids
… set. Following this, several multiple linear regression models were developed, for which Lasso regression was used reduce the number of features, where necessary. The models were developed using a methodology that attempts to reduce the dependency of the results on the identity of the specific …
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Data-Driven Modeling of Tracked Order Vibration in Turbofan Engine
… such as machine learning methods (eg., regression, neural networks), have several drawbacks including lack of interpretability and limited scope, when applying them to a complex multiscale multi-physical dynamical system. Moreover, for dynamical systems with external forcing, the …
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Optical diagnostics for oral and gastrointestinal tract pathologies
… the tissue. Machine learning models, including Lasso regression and Linear Discriminant Analysis (LDA), were employed to identify key spectral features and classify samples with high sensitivity and specificity. This multimodal approach provides complementary information. DRS probes optical …
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Data-driven prediction of rail neutral temperature for continuously welded rails using rail vibration resonance frequencies
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01
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Optimizing Adherence to Antiretroviral Therapy among Adolescents Living with HIV in Low and Middle-Income Settings
… separate covariate-adjusted multilevel logistic regression models were fitted to determine the association between each adherence measure and viral suppression. Aim 2 relied on baseline data, as well. Specifically, for this aim (2), guided by theory, I used selected sociodemographic, behavioral, …
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