Robustness
Year: 2011
Type: article
Abstract: That the conclusion based on a data analysis berobust and stableis not merely a desirable feature, it is essential. To merit this quality label, a conclusion must be supported by strong data‐based evidence and not simply be a discovery gleaned from a preconceived model and weakly supported by a part of the data.Robustness in statisticsrefers to the definition and investigation of procedures that lead to such stability. This article gives a brief overview of the concepts and procedures that are relevant in judging robustness. These have mostly been developed over the last five decades.WIREs Comp Stat2011 3 85–94 DOI: 10.1002/wics.144 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Statistical and Graphical Methods of Data Analysis > Robust Methods
DOI: 10.1002/wics.144
Author: Stephan Morgenthaler
Institution: École Polytechnique Fédérale de Lausanne
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4214700881