We consider the problem of multivariate density estimation when the unknown density is assumed to follow a particular form of dimensionality reduction, a noisy independent factor analysis (IFA) model. In this model the data are generated by a number of latent independent components having...

Abstract We introduce a robust regression estimator for time series factor models called the mOpt estimator. This estimator minimizes the maximum bias due to outlier generating distribution deviations from a standard normal errors distribution factor model, and at the same time has a high normal...

This paper introduces a theory based robust regression estimator, called the mOpt estimator, that minimizes the maximum bias with respect to a Tukey-Huber mixture model that includes a standard linear regression model with normally distribution errors as a special case, but also allows for a...