Statistical models with diverging dimensionality

Nowadays in many statistical applications, we face models whose complexity increases with the sample size. Such models pose a challenge to the traditional statistical analysis, and call for new methodologies and new asymptotic studies, which are exactly the focus of my thesis. In particular, my thesis consists of three parts: i) a novel non-parametric qualification procedure for lysate protein microarray; ii) theoretic analysis for one-way ANOVA with diverging dimensionality and iii) statistical analysis for multi-task learning.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Li, Bin

Contributors dc:contributor

• Qu, Annie
• Liang, Feng
• Marden, John I.
• Portnoy, Stephen L.

• lysate protein microarray
• non-parametric qualification
• regularization
• one-way analysis of variance (ANOVA)
• g-prior
• multi-task learning
• Generalized information criterion (GIC)
• Group Lasso

Statement dc:rights

• Copyright 2013 Bin Li

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/45563

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/45563