The kernel null-space technique is known to be an effective one-class classification (OCC) technique. Nevertheless, the applicability of this method limited due its susceptibility possible training data corruption and inability rank observations according their conformity with model. This article addresses these shortcomings by regularizing solution Fisher methodology in context regression-base...

This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect heavy-tailed errors for high-dimensional robust regression with nonconvex regularization. When additive in linear models have only bounded second moments, we show that iteratively reweighted ?1-penalized adaptive Huber estimator satisfies exponential deviation bounds oracle properties, i...

Using financial ratio data from 2006 and 2007, this study uses a three-fold cross validation scheme to compare the classification pre diction of bankrupt firms by robust logistic regression with Bianco Yohai (BY) estimator versus maximum likelihood (ML) regression. With both 2007 data, BY improves in training set prediction testing set. In an out sample test, correctly predicts bankruptcy for L...