New results in dimension reduction and model selection
Dimension reduction is a vital tool in many areas of applied statistics in which the dimensionality of the predictors can be large. In such cases, many statistical methods will fail or yield unsatisfactory results. However, many data sets of high dimensionality actually contain a much simpler, low...
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Georgia Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1853/22586 |