background and objectives: the large contribution of hospitals to the function and expenditures of the healthsector makes their constant monitoring and evaluation inevitable to improve the overall performance of the health system. built on that, the present study aimed to evaluate the trend of performance of hospitals affiliated with the public hospitals (in guilan province, iran) affiliated wi...

Many statistical M -estimators are based on convex optimization problems formed by the combination of a data-dependent loss function with a norm-based regularizer. We analyze the convergence rates of projected gradient and composite gradient methods for solving such problems, working within a high-dimensional framework that allows the data dimension d to grow with (and possibly exceed) the samp...

Consider the standard linear model y = X + ✏, where the components of ✏ are iid standard normal errors. Park and Casella [14] consider a Bayesian treatment of this model with a Laplace/Inverse-Gamma prior on ( , ). They introduce a Data Augmentation approach that can be used to explore the resulting intractable posterior density, and call it the Bayesian lasso algorithm. In this paper, the Mark...

In this paper, we investigate the degrees of freedom (dof) of penalized `1 minimization (also known as the Lasso) for linear regression models. We give a closed-form expression of the dof of the Lasso response. Namely, we show that for any given Lasso regularization parameter λ and any observed data y belonging to a set of full (Lebesgue) measure, the cardinality of the support of a particular ...

Multiple imputation (MI) is a commonly used technique for handling missing data in large-scale medical and public health studies. However, variable selection on multiply-imputed data remains an important and longstanding statistical problem. If a variable selection method is applied to each imputed dataset separately, it may select different variables for different imputed datasets, which makes...

We propose the use of the Least Absolute Shrinkage and Selection Operator (LASSO) regression method in order to predict the Cumulative Mean Squared Error (CMSE), incurred by the loss of individual slices in video transmission. We extract a number of quality-relevant features from the H.264/AVC video sequences, which are given as input to the LASSO. This method has the benefit of not only keepin...

The Lasso has been widely studied and used in many applications over the last decade. It has also been extended in various directions in particular to ensure asymptotic oracle properties through adaptive weights (Zou, 2006). Another direction has been to incorporate additional knowledge within the penalty to account for some structure among features. Among such strategies the Fused-Lasso (Tibsh...

We develop a novel online learning algorithm for the group lasso in order to efficiently find the important explanatory factors in a grouped manner. Different from traditional batch-mode group lasso algorithms, which suffer from the inefficiency and poor scalability, our proposed algorithm performs in an online mode and scales well: at each iteration one can update the weight vector according t...

The reproduction of a sound field measured using a microphone array is an active topic of research. To this end, loudspeaker and microphone arrays are used. Classical methods rely on spatial transforms (such as spherical Fourier transform for Ambisonics) or pressure matching using least-mean-square formulation. For both methods, all the reproduction sources (i.e. loudspeakers) will typically be...

The Lasso achieves variance reduction and variable selection by solving an 1-regularized least squares problem. Huang (2003) claims that ‘there always exists an interval of regularization parameter values such that the corresponding mean squared prediction error for the Lasso estimator is smaller than for the ordinary least square estimator’. This result is correct. However, its proof in Huang ...