An Analysis of the Indicator Saturation Estimator as a Robust Regression Estimator

An Analysis of the Indicator Saturation Estimator as a Robust Regression Estimator

An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of …nding an estimator that is robust to outliers and structural breaks. This estimator is an example of a one-step M -estimator based on Huber’s skip function. The asymptotic theory is derived in the situation where there are no outliers or structural brea...

A robust inverse regression estimator

A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction via inverse regression: a minimum discrepancy approach. J. Amer. Statist. Assoc. 100, 410–428.] via minimizing a quadratic objective function. Its optimal member called the inverse regression estimator (IRE) was proposed. However, its calculation involves higher order moments of the predictors. ...

Robust nonparametric kernel regression estimator

In robust nonparametric kernel regression context,weprescribemethod to select trimming parameter and bandwidth. Through solving estimating equations, we control outlier effect through combining weighting and trimming. We show asymptotic consistency, establish bias, variance properties and derive asymptotics. © 2016 Elsevier B.V. All rights reserved.

Bayesian Robust Regression with the Horseshoe+ Estimator

The horseshoe+ estimator for Gaussian linear regression models is a novel extension of the horseshoe estimator that enjoys many favourable theoretical properties. We develop the first efficient Gibbs sampling algorithm for the horseshoe+ estimator for linear and logistic regression models. Importantly, our sampling algorithm incorporates robust data models that naturally handle non-Gaussian dat...

A New Biased Estimator Derived from Principal Component Regression Estimator