Contributions to classification and calibration with high-dimensional data
Ge, Nanxiang
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/21457
Description
Title
Contributions to classification and calibration with high-dimensional data
Author(s)
Ge, Nanxiang
Issue Date
1996
Doctoral Committee Chair(s)
Simpson, Douglas G.
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Biology, Biostatistics
Statistics
Language
eng
Abstract
Statistical classification and calibration with high-dimensional data are studied. We have proposed new classification and calibration procedures for high-dimensional data and have established dimensional consistency for certain high-dimensional classification and calibration procedures. Strong dimensional consistency are obtained in certain cases.
By treating data as discretization of random functions, we have proposed smoothing based classification and calibration procedures. We have established consistency and strong consistency for those new procedures and have shown that smoothing based procedures improve the performance when random time-shift exists.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
