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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenMon, 15 Apr 2024 13:26:27 +0000The Dantzig selector: Statistical estimation when p is much larger than n
https://resolver.caltech.edu/CaltechAUTHORS:20100219-092002261
Authors: {'items': [{'id': 'Candes-E', 'name': {'family': 'Candes', 'given': 'Emmanuel'}}, {'id': 'Tao-T', 'name': {'family': 'Tao', 'given': 'Terence'}}]}
Year: 2007
DOI: 10.1214/009053606000001523
In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y=Xβ+z, where β∈Rp is a parameter vector of interest, X is a data matrix with possibly far fewer rows than columns, n≪p, and the zi's are i.i.d. N(0, σ^2). Is it possible to estimate β reliably based on the noisy data y?https://authors.library.caltech.edu/records/484gh-bde27