자료유형  

 학위논문

Control Number  

0015494284

International Standard Book Number  

9781085669160

Dewey Decimal Classification Number  

310

Main Entry-Personal Name  

Tu, Chun-Chen.

Publication, Distribution, etc. (Imprint  

[Sl] : University of Michigan, 2019

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2019

Physical Description  

191 p

General Note  

Source: Dissertations Abstracts International, Volume: 81-02, Section: B.

General Note  

Advisor: Forbes, Florence

Dissertation Note  

Thesis (Ph.D.)--University of Michigan, 2019.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Restrictions on Access Note  

This item must not be added to any third party search indexes.

Summary, Etc.  

요약Regression is a widely used statistical tool to discover associations between variables. Estimated relationships can be further utilized for predicting new observations. Obtaining reliable prediction outcomes is a challenging task. When building a regression model, several difficulties such as high dimensionality in predictors, non-linearity of the associations and outliers could reduce the quality of results. Furthermore, the prediction error increases if the newly acquired data is not processed carefully. In this dissertation, we aim at improving prediction performance by enhancing the model robustness at the training stage and duly handling the query data at the testing stage. We propose two methods to build robust models. One focuses on adopting a parsimonious model to limit the number of parameters and a refinement technique to enhance model robustness. We design the procedure to be carried out on parallel systems and further extend their ability to handle complex and large-scale datasets. The other method restricts the parameter space to avoid the singularity issue and takes up trimming techniques to limit the influence of outlying observations. We build both approaches by using the mixture-modeling principle to accommodate data heterogeneity without uncontrollably increasing model complexity. The proposed procedures for suitably choosing tuning parameters further enhance the ability to determine the sizes of the models according to the richness of the available data. Both methods show their ability to improve prediction performance, compared to existing approaches, in applications such as magnetic resonance vascular fingerprinting and source separation in single-channel polyphonic music, among others. To evaluate model robustness, we develop an efficient approach to generating adversarial samples, which could induce large prediction errors yet are difficult to detect visually. Finally, we propose a preprocessing system to detect and repair different kinds of abnormal testing samples for prediction efficacy, when testing samples are either corrupted or adversarially perturbed.

Subject Added Entry-Topical Term  

Statistics

Added Entry-Corporate Name  

University of Michigan Statistics

Host Item Entry  

Dissertations Abstracts International. 81-02B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:568487