Applications of Robust Regression Techniques: An Econometric Approach

Econometric applications of high - breakdown robust regression techniques

A literature search shows that robust regression techniques are rarely used in applied econometrics. We present a technique based on Rousseeuw and Van Zomeren [Journal of the American Statistical Association, 85 (1990) 633–639] that removes many of the difficulties in applying such techniques to economic data. We demonstrate the value of these techniques by re-analyzing three OLS-based regressi...

Improved Estimation for Robust Econometric Regression Models

The t distribution has proved to be a useful alternative to the normal distribution in many econometric regression models, especially when robust estimation is desired. In this work, we consider a nonlinear heteroskedastic Student t regression model. We suppose the observations to be independently t distributed, with the location and scale parameters for each observation being related to linear...

An inverse problem approach to robust regression

When recording data, large errors may occur occasionally. The corresponding abnormal data points, called outliers, can have drastic effects on the estimates. There are several ways to cope with outliers 0 detect and delete or adjust the erroneous data, use a modified cost function. We propose a new approach that allows, by introducing additional variables, to model the outliers and to detect th...

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

A New Function for Robust Linear Regression: An Iterative Approach

In this paper, we consider solving the robust linear regression problem. We show that IRLS and Newton method can each be combined with preconditioned conjugate gradient least squares method to solve large, sparse, rectangular systems of linear, algebraic equations eeciently. We deene a new function that leads to a cheap preconditioner. Further, for this function, we show that the upper bound on...