Robustness

Abstract

When applying a statistical method in practice, it often occurs that some observations deviate from the usual assumptions. However, many classical methods are sensitive to outliers. The goal of robust statistics is to develop methods that are robust against the possibility that one or several unannounced outliers may occur anywhere in the data .

The Application of Robust Regression to a Production Function Comparison - The Example of Swiss Corn

“…The breakdown point concept is used to quantify robustness properties of an estimator. It is defined as the smallest amount of arbitrary (outlier) contamination, which can carry an estimator over all bounds [24]. The estimator becomes unreliable beyond this borderline.…”

“…Thus, LTS estimation is only used as a data analytic tool for outlier identification. An observation is identified as an outlier if the absolute standardized robust residual (/ i r ) exceeds the cutoff value of 2.5 [24]. Where i r is the (robust) LTS residual and ˆ is the (robust) LTS scale estimate [39].…”

The Application of Robust Regression to a Production Function Comparison~!2008-08-21~!2008-11-10~!2008-11-28~!

“…As a consequence, outliers cause unreliable coefficient estimates if LS is applied [21][22][23][24].…”

Modeling agricultural production risk and the adaptation to climate change

“…The modeling of extreme yield events is inefficient if Ordinary Least Squares (OLS) regression is used for the estimation of coefficients and related residuals. One outlier can be sufficient to move the coefficient estimates arbitrarily far away from the actual underlying values (ROUSSEEUW AND LEROY, 1987, and, HUBERT ET AL., 2004. Thus, analyses based on regression residuals derived by OLS estimation are inefficient and can produce misleading results.…”