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 42 for “"Elastic-Net"”.
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Identification of relevant predictors of loan default using the Elastic Net model
… of a penalised regression approach, namely the Elastic Net model. The study employs a sample of US firms with 162 loan default events in total between 1998 and 2013. The sample is sub-divided to form a Test sample and two holdout samples: one drawn from the same period as the Test sample; and …
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Improving Fast-Scan Cyclic Voltammetry and Raman Spectroscopy Measurements of Dopamine and Serotonin Concentrations via the Elastic Net
… A method of linear regression known as the elastic net was used to make models from the wet lab data. The wetlab data was used to compare the performance of univariate and multivariate type models over various concentration ranges from 0-8000nM of dopamine and serotonin. Cross validation …
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Prediction of CYP3A4 metabolic activity from whole genome RNA-seq data with feature selection machine learning methods
… of the dataset, we applied lasso and elastic net, two feature selection machine learning methods, for prediction and graphical lasso was used for constructing gene network graphs. A simulation study was performed to assess the performance of the prediction algorithms and to evaluate …
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Statistical Modeling for High-dimensional Compositional data with Applications to the Human Microbiome
… variable-selecting models, including LASSO, elastic net, ridge regression and cumulative logit model, to identify the most important subset of variables. This dissertation is structured as follows: </p> <p>Chapter 1 introduces the compositional microbiome data, and then briefly review …
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Theoretical investigations into principles of topographic map formation and applications
… are usually studied within the context of genetic perturbations coupled to high resolution measurements and for these the mouse retinotopic map from retina to superior colliculus has emerged as a useful experimental context. Modelling coupled with genetic perturbation experiments has revealed …
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Sparse Support Matrix Machines for the Classification of Corrupted Data
… hinge loss and regularization terms as spectral elastic net penalty. The regularization term which promotes the structural sparsity and shares similar sparsity patterns across multiple predictors. It is a spectral extension of the conventional elastic net that combines the property of low-rank …
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A Review of 'Big Data' Variable Selection Procedures For Use in Predictive Modeling
… models (such as LASSO, Ridge Regression, and Elastic Net) are simple modifications of multiple linear regression that can provide some guidance on simplifying a model through variable selection. Dimension reducing techniques, such as Partial Least Squares and Principal Components Analysis, are …
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Bayesian and Information-Theoretic Learning of High Dimensional Data
… is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlated features are identified for the response variable. In the sparse Factor Analysis for biomarker trajectories, the high dimensional gene expression data is reduced to a small number of latent factors, each …
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Testing the accuracy of machine learning methods to predict deforestation
… performance than other methods including elastic net regression. Implications of this work go beyond the conservation literature and could be used in other agricultural and applied economic areas where spatial patterns play a significant role.
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Three essays on econometrics: Network estimators with applications and assessment of the effects of Covid-19 pandemic
… first two of them, aims to develop estimators of network structures from observations of a random vector whose components represent the variables of interest and are the nodes in a network. The goal is to reconstruct a (weighted) graph when we are not able to directly observe connections among …
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Stereotype Logit Models for High Dimensional Data
… regression model (Anderson, 1984) with an elastic net penalty (Friedman et al., 2010) as a method capable of modeling an ordinal outcome for high-throughput genomic datasets. Results from applying the proposed method to both simulated and gene expression data will be reported and the …
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Anatomically aware machine learning modeling of PET count rates for detection and quantification of radiotracer extravasation
… της Γραμμικής Παλινδρόμησης, του Elastic Net, του Random Forest και του XGBoost, με σκοπό την πρόβλεψη των ρυθμών καταμέτρησης ανά ανατομική περιοχή. Η απόδοση των μοντέλων αξιολογήθηκε με τη χρήση τυπικών μετρικών (RMSE, MAE και R2) καθώς και με διασταυρούμενη επικύρωση …
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Assessment of Penalized Regression for Genome-wide Association Studies
… regions for follow-up. We recommend using the Elastic Net with a mixing weight for the Lasso penalty near 0.5 as the best method.
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Development of a Machine Learning Algorithm for the Estimation of Soil Organic Matter from the Integration of UAV and In-Ground Soil Sensor
… of algorithms was tried in this study; however, elastic net, ridge, and linear regression were the most effective. The RMSE (Root Mean Square Value) of 0.13, 0.12, 0.13 and the R2 coefficient of determination of 0.13, 0.16, 0.12 showed that the suggested model fits well and accounts for 14% of …
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Systems Pharmacology – Machine Learning Approaches in Profiling Oncology Drug Candidates
… machines (SVMs), Naïve Bayes, Artificial neural nets (ANN), and Decision trees – classification and regression tree (CART) and multi-tree majority voting ensemble techniques i.e., random forest and XGBoost.The feature sets for building these models were extracted by computing chemical …
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Development of a Machine Learning Algorithm for the Estimation of Soil Organic Matter from the Integration of UAV and In-Ground Soil Sensor
… of algorithms was tried in this study; however, elastic net, ridge, and linear regression were the most effective. The RMSE (Root Mean Square Value) of 0.13, 0.12, 0.13 and the R2 coefficient of determination of 0.13, 0.16, 0.12 showed that the suggested model fits well and accounts for 14% of …
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The Association between the Immune Proteome and Brain Function and Socioemotional Development in 5-Year-Old Children
… (SDQ), immune proteomics, and functional magnetic resonance imaging (fMRI) in 5-year-old children from the FinnBrain Birth Cohort Study. Associations between immune proteome and both socioemotional functioning and brain connectivity were examined using data-driven feature selection methods. …
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Deconvolute Brain Tumor Genomic Alterations Based On Dna Methylation
… gliomas. However, the association between epigenetic signature and genetic alterations is poorly understood. For example, mutation of isocitrate dehydrogenase (<em>IDH</em>) is associated with genome-wide hypermethylation of CpG islands in gliomas. But other subtype-associated alterations, …
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Dietary and Serum Biomarkers, and Glycaemic status: Relationships with Type 2 Diabetes Mellitus and Incident Dementia
… biomarker-glycaemic status associations, while elastic net regression selected key predictors; and cox proportional hazards models examined dementia risk over time. Mediation and moderation analyses explored the role of T2D in biomarker and dietary relationships with dementia. Results: …
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CFD Modelling of the Mixture Preparation in a Modern Gasoline Direct Injection Engine and Correlations with Experimental PN Emissions
… prediction when comparing numerical spray tip penetration and droplet size characteristics to the experimental counterparts. Then, the modelling protocol incorporated droplet-wall interaction models and a multi-component surrogate fuel blend model. The comprehensive digital model was validated …
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