Deciding the Dimension of Effective Dimension Reduction Space

Event Details
  • Date/Time:
    • Thursday September 10, 2009
      11:00 am - 12:00 pm
  • Location: Executive classroom
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    $0.00
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Contact
Nagi Gebraeel
ISyE
Contact Nagi Gebraeel
404-894-0054
Summaries

Summary Sentence: Deciding the Dimension of Effective Dimension Reduction Space

Full Summary: Deciding the Dimension of Effective Dimension Reduction Space for Functional and High Dimensional Data

TITLE: Deciding the Dimension of Effective Dimension Reduction Space for Functional and High Dimensional Data

SPEAKER: Dr. Yehua Li
Dept. of Statistics, UGA

ABSTRACT:

In this paper, we consider regression models with a functional predictor and a scalar response, where the response depends on the predictor only through a finite number of projections. The linear subspace spanned by these projections is called the effective dimension reduction (EDR) space. To determine the dimensionality of the EDR space, we focus on the principal component scores of the functional predictor, and propose two sequential chi-square testing procedures under the assumption that the predictor has an elliptically contoured distribution. We further extend these procedures and introduce an adaptive Neyman test that simultaneously takes into account a large number of principal component scores. These tests can be used for model building or as goodness-of-fit tests in the context of functional linear models and functional projection pursuit models. The proposed procedures are supported by theory, validated by simulation studies, and illustrated by a real-data example. Although our methods and theory were developed under the functional data framework, they are applicable to general high-dimensional data.

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H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

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Seminar/Lecture/Colloquium
Keywords
EDR
Status
  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Oct 12, 2009 - 4:16pm
  • Last Updated: Oct 7, 2016 - 9:46pm