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You searched for +publisher:"North Carolina State University" +contributor:("Dr. Hao Helen Zhang, Committee Co-Chair"). Showing records 1 – 2 of 2 total matches.

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North Carolina State University

1. Huang, Lingkang. Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis.

Degree: PhD, Statistics, 2009, North Carolina State University

Microarray techniques provide new insights into cancer diagnosis using gene expression profiles. Molecular diagnosis based on high-throughput genomic data sets presents major challenge due to the overwhelming number of variables and complex multi-class nature of tumor samples. In this thesis, the author first tackled a multi-class problem related to liver toxicity severity prediction using the Random Forest and GEMS-SVM (Gene Expression Model Selector using Support Vector Machine). However, the original SVM regularization formulation does not accommodate the variable selection. Most existing approaches, including GEMS-SVM, handle this issue by selecting genes prior to classification, which does not consider the correlation among genes since they are selected by univariate ranking. In this thesis, the author developed new multi-class SVM (MSVM) approaches which can perform multi-class classification and variable selection simultaneously and learn optimal classifiers by considering all classes and all genes at the same time. The original multi-class SVM proposed by Crammer and Singer (2001) does not perform the variable selection. By using the MSVM loss function proposed by Crammer and Singer (2001), the author developed new variable selection approaches for both linear and non-linear classification problems. For linear classification problems, four different sparse regularization terms were included in the objective function respectively. For nonlinear classification problems, two different approaches have been developed to tackle them. The first approach was used in non-linear MSVMs via basis function transformation. The second approach was used in non-linear MSVMs via kernel functions. The newly developed methods were applied to both simulation and real data sets. The results demonstrated that our methods could select a much smaller number of genes, compared with other existing methods, with high classification accuracy to predict the tumor subtypes. The combination of high accuracy and small number of genes makes our new methods as powerful tools for disease diagnostics based on expression data and target identifications of the therapeutic intervention. Advisors/Committee Members: Dr. Zhao-Bang Zeng, Committee Chair (advisor), Dr. Hao Helen Zhang, Committee Co-Chair (advisor).

Subjects/Keywords: multi-class classification; support vector machine; microarray; variable selection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, L. (2009). Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5240

Chicago Manual of Style (16th Edition):

Huang, Lingkang. “Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis.” 2009. Doctoral Dissertation, North Carolina State University. Accessed November 18, 2019. http://www.lib.ncsu.edu/resolver/1840.16/5240.

MLA Handbook (7th Edition):

Huang, Lingkang. “Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis.” 2009. Web. 18 Nov 2019.

Vancouver:

Huang L. Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2019 Nov 18]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5240.

Council of Science Editors:

Huang L. Variable Selection in Multiclass Support Vector Machine and Applications in Genomic Data Analysis. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5240


North Carolina State University

2. Shows, Justin Hall. Sparse Estimation and Inference for Censored Median Regression.

Degree: PhD, Statistics, 2009, North Carolina State University

Censored median regression models have been shown to be useful for analyzing a variety of censored survival data with the robustness property. We study sparse estimation and inference of censored median regression. The new method minimizes an inverse censoring probability weighted least absolute deviation subject to the adaptive LASSO penalty. We show that, with a proper choice of the tuning parameter, the proposed estimator has nice theoretical properties such as root-n consistency and asymptotic normality. The estimator can also identify the underlying sparse model consistently. We propose using a resampling method to estimate the variance of the proposed estimator. Furthermore, the new procedure enjoys great advantages in computation, since its entire solution path can be obtained efficiently. Also, the method can be extended to multivariate survival data, where there is a natural or artificial clustering structure. The performance of our estimator is evaluated by extensive simulations and two real data applications. Advisors/Committee Members: Dr. Wenbin Lu, Committee Chair (advisor), Dr. Hao Helen Zhang, Committee Co-Chair (advisor), Dr. Dennis Boos, Committee Member (advisor), Dr. Daowen Zhang, Committee Member (advisor).

Subjects/Keywords: censored data; median regression; variable selection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shows, J. H. (2009). Sparse Estimation and Inference for Censored Median Regression. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/5565

Chicago Manual of Style (16th Edition):

Shows, Justin Hall. “Sparse Estimation and Inference for Censored Median Regression.” 2009. Doctoral Dissertation, North Carolina State University. Accessed November 18, 2019. http://www.lib.ncsu.edu/resolver/1840.16/5565.

MLA Handbook (7th Edition):

Shows, Justin Hall. “Sparse Estimation and Inference for Censored Median Regression.” 2009. Web. 18 Nov 2019.

Vancouver:

Shows JH. Sparse Estimation and Inference for Censored Median Regression. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2019 Nov 18]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5565.

Council of Science Editors:

Shows JH. Sparse Estimation and Inference for Censored Median Regression. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/5565

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