Econometric applications of high - breakdown robust regression techniques

SUGI 27: Robust Regression and Outlier Detection with the ROBUSTREG Procedure

Robust regression is an important tool for analyzing data that are contaminated with outliers. It can be used to detect outliers and to provide resistant (stable) results in the presence of outliers. This paper introduces the ROBUSTREG procedure, which is experimental in SAS/STAT Version 9. The ROBUSTREG procedure implements the most commonly used robust regression techniques. These include M ...

Two Robust Fuzzy Regression Models and Their Applications in Predicting Imperfections of Cotton Yarn

High Breakdown Multivariate Estimators

In the literature, estimators for regression or multivariate location and dispersion that have been shown to be both consistent and high breakdown are impractical to compute. This paper gives easily computed high breakdown robust √ n consistent estimators, and the applications for these estimators are numerous. For regression, the response plot of the fitted values versus the response is shown ...

Robust Regression

1. Introduction One of the most important statistical tools is a linear regression analysis for many fields. Nearly all regression analysis relies on the method of least squares for estimation of the parameters in the model. A problem that we often encountered in the application of regression is the presence of an outlier or outliers in the data. Outliers can be generated by from a simple opera...

The Conditional Breakdown Properties of Robust Local Polynomial Estimators

Nonparametric regression techniques provide an e ective way of identifying and examining structure in regression data The standard approaches to nonparametric regression such as local polynomial and smoothing spline estimators are sensitive to unusual observations and alternatives designed to be resistant to such observations have been proposed as a solution Unfortunately there has been little ...