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Group LASSO for Prediction of Clinical Outcomes in Cancer

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dc.contributor.advisor Wang, Xuefeng en_US
dc.contributor.advisor Zhu, Wei en_US
dc.contributor.author Tian, Xinyu en_US
dc.contributor.other Department of Applied Mathematics and Statistics en_US
dc.date.accessioned 2017-09-20T16:52:32Z
dc.date.available 2017-09-20T16:52:32Z
dc.date.issued 2017-05-01 en_US
dc.identifier.uri http://hdl.handle.net/11401/77332 en_US
dc.description 122 pg. en_US
dc.description.abstract High-dimensional datasets are now ubiquitous in biomedical research. Feature selection is an essential step in mining high-dim data to reduce noise, avoid overfitting and improve the interpretation of statistical models. In the last few decades, numerous feature selection methods and algorithms have been proposed for various response types, connections in predictors and requirements on sparsities; and penalized methods, such as LASSO and its variations, are the most efficient and popular ones in this area. In addition, genomic features, such as gene expressions, are usually connected through an underlying biological network, which is an important supplement to the model in improving performance and interpretability. In this study, we first extend the group LASSO to a network-constrained classification model and develop a modified proximal gradient algorithm for the model fitting. In this algorithm, group lasso regularization is used to induce model sparsity, and a network constraint is imposed to induce the smoothness of the coefficients using underlying network structure. The applicability of the proposed method is verified by analyzing both numerical examples and real gene expression data in TCGA. We further work on the feature selection problem with Bayesian hierarchical structure. R. Tibshirani, who introduced LASSO in 1996, also proposed that linear LASSO can be considered as a Bayesian model with Laplace prior on coefficient parameters, which shed lights on the feature selection problem in Bayesian models. Compared to frequentist approaches, Bayesian model copes better with complex hierarchical structures of the data. On one hand, we compare the performance of Laplace, horseshoe and Gaussian priors in linear Bayesian models with extensive simulations. On the other, we extend the projection predictive feature selection scheme to group-wise selection and benchmark its feature selection performance and prediction accuracy with standard Bayesian methods. All Bayesian posterior parameters are estimated using Hamiltonian Monte Carlo implemented in Stan. en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Statistics en_US
dc.subject.other Bayesian, feature selection, LASSO, Network constraint, proximal gradient, Stan en_US
dc.title Group LASSO for Prediction of Clinical Outcomes in Cancer en_US
dc.type Dissertation en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Kuan, Pei Fen en_US
dc.contributor.committeemember Yu, Xiaxia. en_US

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