The accelerated failure time (AFT) model assumes a linear relationship between the event time and the covariates. We propose a robust weighted least-absolute-deviations (LAD) method for estimation in the AFT model with right-censored data. This method uses the Kaplan-Meier weights in the LAD objective function to account for censoring. We show that the proposed estimator is root-n consistent an...

The support vector machine (SVM) is a widely used method for classification. Although many efforts have been devoted to develop efficient solvers, it remains challenging to apply SVM to large-scale problems. A nice property of SVM is that the non-support vectors have no effect on the resulting classifier. Motivated by this observation, we present fast and efficient screening rules to discard no...

We consider a problem in signal processing which deals with the recovery of a high-dimensional sparse signal based on a small number of measurements. Our goal is to apply the least absolute deviations (LAD) method in an algorithm that would essentially follow the steps of the orthogonal matching pursuit (OMP) algorithm that has been used mostly in this setting. OMP can recover the signal with h...

A major challenge in single particle reconstruction from cryo-electron microscopy is to establish a reliable ab initio three-dimensional model using two-dimensional projection images with unknown orientations. Common-lines-based methods estimate the orientations without additional geometric information. However, such methods fail when the detection rate of common-lines is too low due to the hig...

A learning machine--or a model--is usually trained by minimizing a given criterion (the expectation of the cost function), measuring the discrepancy between the model output and the desired output. As is already well known, the choice of the cost function has a profound impact on the probabilistic interpretation of the output of the model, after training. In this work, we use the calculus of va...

Estimation of trip tables and other matrices that are subject to constraints is a common practical problem. This note reviews four common estimation methods: (1) minimization of the sum of absolute deviations, (2) the biproportional technique, (3) information minimization and (4) constrained generalized least squares (CGLS) regression. A small example illustrates their application. Computationa...

Practical considerations for choosing between Tobit, symmetrically censored least squares (SCLS) and censored least absolute deviations (CLAD) estimators are offered. Practical considerations deal with when a Hausman test is better than a conditional moment test for judging the severity of a misspecification, the need to bootstrap the sampling distributions of theHausman tests, what to look for...

In this paper, a novel noise-constrained least-squares (NCLS) method for online autoregressive (AR) parameter estimation is developed under blind Gaussian noise environments, and a discrete-time learning algorithm with a fixed step length is proposed. It is shown that the proposed learning algorithm converges globally to an AR optimal estimate. Compared with conventional second-order and high-o...

The performance of active portfolio methods critically depends on the forecasting ability of the security analyst. The Treynor-Black model provides an efficient way of implementing active investment strategy. Despite its potential benefits, the Treynor-Black model appears to have had little impact on the financial community, mainly because it has been believed that the precision threshold of al...

Clinically relevant cardiovascular parameters, such as pulmonary blood volume (PBV) and ejection fraction (EF), can be assessed through indicator dilution techniques. Among these techniques, which are typically invasive due to the need for central catheterization, contrast ultrasonography provides a new emerging minimally invasive option. PBV and EF are then measured by a dilution system identi...