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1. Matrix Distances With their Application to Finding Directional Deviations from normality in High-Dimensional Data

open_access
Open Access
Author:
Hui, Guodong
Title:
Matrix Distances With their Application to Finding Directional Deviations from normality in High-Dimensional Data
Graduate Program:
Statistics
Keywords:
non-normal direction
quadratic distance
eigenanalysis
Fisher information
projection pursuit
mixture model
File:
Download phd_thesis_guodong_hui.pdf
Committee Members:
Bruce G Lindsay, Committee Chair/Co-Chair
Thomas P Hettmansperger, Committee Member
Runze Li, Committee Member
Jesse Louis Barlow, Committee Member

2. COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT DIMENSION REDUCTION & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS

open_access
Open Access
Author:
Nandy, Debmalya
Title:
COVARIATE INFORMATION: A NOVEL APPROACH TO SUFFICIENT DIMENSION REDUCTION & FEATURE SCREENING FOR ULTRAHIGH-DIMENSIONAL COVARIATES IN SUPERVISED PROBLEMS
Graduate Program:
Statistics
Keywords:
Fisher information
Density information
Supervised problems
Sufficient dimension reduction
Feature screening
Ultrahigh dimension
Bootstrap
File:
Download PennState_StatisticsPhDDissertation_Final_eTD_081619.pdf
Committee Members:
Francesca Chiaromonte, Dissertation Advisor/Co-Advisor
Francesca Chiaromonte, Committee Chair/Co-Chair
Runze Li, Committee Member
Bing Li, Committee Member
Kateryna Dmytrivna Makova, Outside Member
Lingzhou Xue, Committee Member
Weixin Yao, Special Member