Inferences on high-dimensional data

"Dimension reduction techniques are important in the problem of regression and prediction when the nominal number of predicting variables is greater than the number of observations. Two methods, principal components analysis (PCA) and partial least squares (PLS), are used for regression and classification. We show that the null distribution of the PLS ""f-test"" statistic, which is obtained from one factor PLS regression, depends heavily on the design. A simulation method is suggested to compute the appropriate significant level of the ""f-test"". Some of the statistical properties of the composite dimensional reduction procedures are derived."

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

2011

Author dc:creator

• Guo, Sha-Lin

Contributors dc:contributor

• Simpson, Douglas G.

• Statistics

Statement dc:rights

• Copyright 1990 Guo, Sha-Lin

Language dc:language

eng

Identifier

AAI9114251
(UMI)AAI9114251

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/23507