Several problems arising in electromagnetics can be directly formulated or suitably recast for
an effective solution within the compressive sensing (CS) framework. This has motivated a …

We present an expository, general analysis of valid post-selection or post-regularization
inference about a low-dimensional target parameter in the presence of a very high …

Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional
data, due to their simple geometric interpretations and typically attractive computational …

Ordinary least squares and stepwise selection are widespread in behavioral science
research; however, these methods are well known to encounter overfitting problems such …

We study full Bayesian procedures for high-dimensional linear regression under sparsity
constraints. The prior is a mixture of point masses at zero and continuous distributions …

SLOPE—ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION - PMC Back to
Top Skip to main content NIH NLM Logo Access keys NCBI Homepage MyNCBI Homepage …

We present a new method for performing differential emission measure (DEM) inversions on
narrow-band EUV images from the Atmospheric Imaging Assembly (AIA) on board the Solar …

Non-smooth regularized convex optimization procedures have emerged as a powerful tool
to recover structured signals (sparse, low-rank, etc.) from (possibly compressed) noisy linear …

Abstract Slepian and Sudakov–Fernique type inequalities, which compare expectations of
maxima of Gaussian random vectors under certain restrictions on the covariance matrices …

This paper establishes non-asymptotic oracle inequalities for the prediction error and
estimation accuracy of the LASSO in stationary vector autoregressive models. These …