BACKGROUND Restless legs syndrome (RLS) is a chronic sensory-motor disorder characterized by unpleasant limb sensations and an irresistible urge to move. The International Restless Legs Syndrome Study Group developed the Restless Legs Syndrome Rating Scale (IRLS) to assess the severity of RLS symptoms. The objective of this study was to translate and validate the IRLS into Brazilian Portuguese....

For solving a wide class of nonconvex and nonsmooth problems, we propose a proximal linearized iteratively reweighted least squares (PL-IRLS) algorithm. We first approximate the original problem by smoothing methods, and second write the approximated problem into an auxiliary problem by introducing new variables. PL-IRLS is then built on solving the auxiliary problem by utilizing the proximal l...

In this paper, we study the theoretical properties of a class of iteratively re-weighted least squares (IRLS) algorithms for sparse signal recovery in the presence of noise. We demonstrate a one-to-one correspondence between this class of algorithms and a class of Expectation-Maximization (EM) algorithms for constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribu...

PATIENTS AND METHODS To assess the reliability, validity, and responsiveness of the International Restless Legs Syndrome Study Group's rating scale (the International Restless Legs Scale (IRLS)) (V2.0), using pooled data from two matching, placebo-controlled studies of ropinirole for treating Restless Legs Syndrome (RLS). RESULTS Pooled patient samples comprised 550 patients in the baseline (...

Iteratively Re-weighted Least Squares minimization (IRLS) appears for the first time in the approximation practice in the Ph.D. thesis of C. L. Lawson in 1961 for L∞ minimization. In the 1970s extensions of Lawson’s algorithm for `p-minimization were proposed, as reported in the work of M. R. Osborne. IRLS has been proposed for sparse recovery in signal processing in [5] and for total variation...

In this paper, we study the theoretical properties of a class of iteratively re-weighted least squares (IRLS) algorithms for sparse signal recovery in the presence of noise. We demonstrate a one-toone correspondence between this class of algorithms and a class of Expectation-Maximization (EM) algorithms for constrained maximum likelihood estimation under a Gaussian scale mixture (GSM) distribut...

Robust regression techniques are a class of estimators that are relatively insensitive to the presence of one or more outliers in the data. They are especially well suited to data that require large numbers of statistical tests and may contain outliers due to factors not of experimental interest. Both these issues apply particularly to neuroimaging data analysis. We use simulations to compare s...