Lasso-type sparse regression and high-dimensional Gaussian graphical models
Published: Jan 1, 2012
Abstract
High-dimensional datasets, where the number of measured variables is larger than the sample size, are not uncommon in modern real-world applications such as functional Magnetic Resonance Imaging (fMRI) data. Conventional statistical signal processing tools and mathematical models could fail at handling those datasets. Therefore, developing statistically valid models and computationally efficient algorithms for highdimensional situations are of...
Paper Details
Title
Lasso-type sparse regression and high-dimensional Gaussian graphical models
Published Date
Jan 1, 2012
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