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Estimation Of Sparse Low-Dimensional Linear Projections

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Abstract

Many multivariate analysis problems are unified under the framework of linear projections. These projections can be tailored towards the analysis of variance (principal components), classification (discriminant analysis) or network recovery (canonical correlation analysis). Traditional techniques form these projections by using all of the original variables, however in recent years there has been a lot of interest in performing variable selection. The main goal of this dissertation is to elucidate some of the fundamental issues that arise in highdimensional multivariate analysis and provide computationally efficient and theoretically sound alternatives to existing heuristic techniques

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2015-05-24

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multivariate analysis; high-dimensional statistics; classification

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Union Local

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Committee Chair

Booth,James

Committee Co-Chair

Wells,Martin Timothy

Committee Member

Mezey,Jason G.
Wegkamp,Marten H.

Degree Discipline

Statistics

Degree Name

Ph. D., Statistics

Degree Level

Doctor of Philosophy

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Government Document

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dissertation or thesis

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