Robust statistics are statistics which maintain their properties even if the underlying distributional assumptions are incorrect. Robust statistical methods...

Hettmansperger, T. P.; McKean, J. W. (1998). Robust Nonparametric Statistical Methods. Kendall's Library of Statistics. Vol. 5. London: Edward Arnold. ISBN 0-340-54937-8...

the median. For this reason, the median is of central importance in robust statistics. The median of a finite list of numbers is the "middle" number, when...

resistant to outliers. They thus are useful in robust statistics, as descriptive statistics, in statistics education, and when computation is difficult...

In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...

related to two other developments in statistical theory: robust statistics and nonparametric statistics, both of which tried to reduce the sensitivity of statistical...

more robust to outliers, so that if the Gaussian model is questionable or approximate, there may advantages to using the median (see Robust statistics)....

method helped popularize robust statistics in computer vision. This was facilitated by several papers that connected robust penalty functions to classical...

of robust statistics, pivotal quantities are robust to changes in the parameters — indeed, independent of the parameters — but not in general robust to...

mean may not coincide with one's notion of "middle". In that case, robust statistics, such as the median, may provide a better description of central tendency...