Sufficient dimension reduction and variable selection.
2010-12
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Sufficient dimension reduction and variable selection.
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2010-12
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Abstract
Sufficient dimension reduction (SDR) in regression was first introduced by Cook
(2004). It reduces the dimension of the predictor space without loss of information and
it is very helpful when the number of predictors is large. It alleviates the “curse of
dimensionality” for many statistical methods. In this thesis, we study the properties
of a dimension reduction method named “continuum regression”; we propose a unified
method – coordinate-independent sparse estimation (CISE) – that can simultaneously
achieve sparse sufficient dimension reduction and screen out irrelevant and redundant
variables efficiently; we also introduce a new dimension reduction method called “principal
envelope models”.
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University of Minnesota Ph.D. dissertation. December 2010. Major: Statistics. Advisor: R. Dennis Cook. 1 computer file (PDF); vii, 69 pages, appendix p. 59-64.
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Chen, Xin. (2010). Sufficient dimension reduction and variable selection.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/99484.
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