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 18 of 18 for “"penalized regression"”.
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Assessment of Penalized Regression for Genome-wide Association Studies
… simultaneously. This approach requires a form of Penalized Regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by False Discovery Rate (FDR) control, and assess their …
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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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Three Essays on Causal Inference With Model Averaging
… simulations show that the model averaging and penalized regression methods yield more accurate counterfactual prediction than the model selection methods. We also find evidences that if the predictors (e.g., control units' outcomes) are more correlated, the model averaging methods have more …
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Statistical methods for modeling RNA-Seq short-read data
… calculate RNA expression, the first using a penalized regression approach to remove bias based on nucleotide composition, as well as a second which demonstrates the use of variation as an estimate of gene expression. Another method is developed which utilizes RNA-Seq gene expression data to …
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Penalized discriminant analysis for multivariate functional data
We introduce a penalized discriminant analysis method for multivariate functional data supported on compact 1D domains, motivated by an application that aims to identify subjects with poor cognitive status from diffusion MRI data. By leveraging a connection to the optimal scoring problem, we bypass …
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Variable screening and model selection in censored quantile regression via sparse penalties and stepwise refinement
… selection methods are available for linear regression but very little has been developed for quantile regression, especially for the censored problems. This study will look at the possibilities of utilizing some existing penalty variable selection methods on censored quantile regression …
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A Review of 'Big Data' Variable Selection Procedures For Use in Predictive Modeling
… on ‘big data.’ For instance, multiple linear regression models cannot be used on datasets with hundreds of variables. However several techniques are becoming common tools for selective inference as the need for analyzing big data increases. Forward selection and penalized regression models …
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Failure Diagnosis for Datacenter Applications
… component interactions and dependencies, (2) a penalized-regression-based failure localization algorithm that localizes both fail-stop and gray failures, and (3) a network architecture that produces predictable routes, simplifying failure localization without sacrificing load balancing and other …
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Modeling Areal Measures of Campsite Impacts on the Appalachian National Scenic Trail, USA Using Airborne LiDAR and Field Collected Data
… Shrinkage and Selection Operator (LASSO) penalized regression for factor selection and Ordinary Least Squares (OLS) for regressions. Chosen variables in regressions explained 64% of the variation in campsite size and 61% of the variation in the area of vegetation loss on a campsite. …
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The predictive relationship of executive functioning to social-emotional functioning for school-aged children in Kenya
… functioning in this study. A series of multiple regression analyses were used to assess the degree to which ratings on the between BRIEF-2 TF Composites predicted ratings on BASC-3 TRS composites as well as the ratings on the Executive Functioning and Emotional Self-Control Indices on the BASC-3 …
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Improving Clinical Risk Models through Integration of Polygenic Risk Scores and Omics
… causal omics biomarkers for CAD and propose a penalized regression framework integrative PRS model to improve risk prediction in an African population when limited to a European GWAS and reference panel.
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Integrative Biomarker Identification and Classification Using High Throughput Assays
… and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific …
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Application and Extension of Weighted Quantile Sum Regression for the Development of a Clinical Risk Prediction Tool
… the HSM using Weighted Quantile Sum (WQS) regression (Gennings et al., 2013; Carrico, 2013), a novel penalized regression technique that imposes nonnegativity and unit-sum constraints on the coefficients used to weight index components. In this dissertation, we develop a number of …
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Heterogeneity modeling and longitudinal clustering
… to subgroup longitudinal profiles using a penalized regression method. We utilize a pairwise-grouping penalization on the parameters corresponding to the individual nonparametric B-spline models, and thereby identify clusters based on different patterns of the predicted longitudinal curves. …
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Testing new genetic and genomic approaches for trait mapping and prediction in wheat (Triticum aestivum) and rice (Oryza spp)
… been proposed for genomic prediction. The ridge regression best linear unbiased prediction (RR-BLUP) is based on the assumption that all genotyped molecular markers make equal contributions to the variations of a phenotype. Information from underlying candidate molecular markers are however of …
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Essays on Regulations in Peer-To-Peer Markets
… results are stable across different choices of Penalized Regression methods. The findings also indicate that a good model fit in the first stage is crucial to the validity of the final estimates. Finally, the chapter shows how biases from ignoring high dimensional features in estimating residual …
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Contributions to Structured Variable Selection Towards Enhancing Model Interpretation and Computation Efficiency
… in the generalized linear model such as logistic regression. This work is motivated from the engineering problem with varying effects of process variables to product quality caused by equipment degradation. To address such challenge, we propose a penalized dynamic regression model which is …
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The complexity of bioinformatics: techniques for addressing the combinatorial explosion in proteomics and genomics
… biological condition. By employing multivariate penalized regression, the system described is capable of efficiently identifying transcription factor binding motifs whose presence strongly correlates with gene expression in the measured biological condition from amongst hundreds of candidates. A …