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Title:Statistical models with diverging dimensionality
Author(s):Li, Bin
Director of Research:Qu, Annie
Doctoral Committee Chair(s):Liang, Feng
Doctoral Committee Member(s):Qu, Annie; Marden, John I.; Portnoy, Stephen L.
Department / Program:Statistics
Discipline:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):lysate protein microarray
non-parametric qualification
regularization
one-way analysis of variance (ANOVA)
g-prior
multi-task learning
Generalized information criterion (GIC)
Group Lasso
Abstract: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.
Issue Date:2013-08-22
URI:http://hdl.handle.net/2142/45563
Rights Information:Copyright 2013 Bin Li
Date Available in IDEALS:2013-08-22
2015-08-22
Date Deposited:2013-08


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