A unified view of high-dimensional bridge regression
In many application areas ranging from bioinformatics to imaging, we are interested in recovering a sparse coefficient in the high-dimensional linear model, when the sample size n is comparable to or less than the dimension p. One of the most popular classes of estimators is the Lq-regularized least...
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| Language: | English |
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2017
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| Online Access: | https://doi.org/10.7916/D82V2THP |