Doctoral Dissertations

Lei Huo

Keywords and Phrases

Conditional Screening; High Dimensional Data; Partial Central Subspace; Sufficient Dimension Reduction; Trace Pursuit; Variable Selection

Abstract

"Variable selection becomes more crucial than before, since high dimensional data are frequently seen in many research areas. Many model-based variable selection methods have been developed. However, the performance might be poor when the model is mis-specified. Sufficient dimension reduction (SDR, Li 1991; Cook 1998) provides a general framework for model-free variable selection methods.

In this thesis, we first propose a novel model-free variable selection method to deal with multi-population data by incorporating the grouping information. Theoretical properties of our proposed method are also presented. Simulation studies show that our new method significantly improves the selection performance compared with those ignoring the grouping information. In the second part of this dissertation, we apply partial SDR method to conduct conditional model-free variable (feature) screening for ultra-high dimensional data, when researchers have prior information regarding the importance of certain predictors based on experience or previous investigations. Comparing to the state of art conditional screening method, conditional sure independence screening (CSIS; Barut, Fan and Verhasselt, 2016), our method greatly outperforms CSIS for nonlinear models. The sure screening consistency property of our proposed method is also established"--Abstract, page iv.

Advisor(s)

Wen, Xuerong Meggie

Committee Member(s)

Samaranayake, V. A.
Adekpedjou, Akim
Olbricht, Gayla R.
Jiang, Wei

Department(s)

Mathematics and Statistics

Degree Name

Ph. D. in Mathematics

Comments

Doctor of Philosophy in Mathematics with a Statistics Emphasis

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2018

Journal article titles appearing in thesis/dissertation

  • Trace pursuit variable selection for multi-population data
  • A model-free conditional screening approach via sufficient dimension reduction

Pagination

vii, 70 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2018 Lei Huo, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11714

Electronic OCLC #

1164805570

Download

Share

 
COinS