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Robustness
Author(s) -
Morgenthaler Stephan
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.144
Subject(s) - robustness (evolution) , computer science , nonparametric statistics , exploratory data analysis , statistical analysis , data mining , machine learning , artificial intelligence , data science , statistics , mathematics , biochemistry , chemistry , gene
That the conclusion based on a data analysis be robust and stable is 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 statistics refers 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 Stat 2011 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