5 accepted papers
This work establishes regularity conditions for consistency and asymptotic normality of the multiple parameter maximum likelihood estimator (MLE) from censored data, where the censoring mechanism is in the form of 1-bit measurements. The underlying distribution of the uncensored data is assumed to b…
This work considers a co-located MIMO radar with M<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</inf> transmitting and M<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> receiving antennas in a so…
We determine statistical and computational limits for estimation of a rank-one matrix (the spike) corrupted by an additive gaussian noise matrix, in a sparse limit, where the underlying hidden vector (that constructs the rank-one matrix) has a number of non-zero components that scales sub-linearly w…
The LASSO is a well-studied method for use in high-dimensional linear regression where one wishes to recover a sparse vector b from noisy observations y measured through a n-by-p matrix X with the model y = Xb + w where w is a vector of independent, mean-zero noise. We study the linear asymptotic r…
SLOPE is a relatively new convex optimization procedure for high-dimensional linear regression via the sorted $\ell_1$ penalty: the larger the rank of the fitted coefficient, the larger the penalty. This non-separable penalty renders many existing techniques invalid or inconclusive in analyzing the…