Robust regression methods are designed to limit the effect that violations of assumptions by the underlying data-generating process have on regression estimates ...

Robust regression methods provide an alternative to least squares regression by requiring less restrictive assumptions. These methods attempt to dampen the ...

We say that an estimator or statistical procedure is robust if it provides useful information even if some of the assumptions used to justify the estimation ...

Robust regression uses a method called iteratively reweighted least squares to assign a weight to each data point. This method is less sensitive to large ...

Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for ...

29 мая 2021 г. · A robust regression is an iterative procedure that is designed to overcome the problem of outliers and influential observations in the data and ...

In this section, we present an overview of some of the most useful robust estimators for regression.

Robust regression methods provide an alternative to least squares regression by requiring less restrictive assumptions. These methods attempt to dampen the ...

In this article four robust regression techniques that combine high breakdown points and high efficiency are presented. The breakdown point is a global measure ...