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A robust analysis of unreplicated factorials
Author(s) -
AguirreTorres Víctor,
Vara Román
Publication year - 2012
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.938
Subject(s) - outlier , computer science , fractional factorial design , robust regression , robust statistics , regression , statistics , contingency , factorial experiment , econometrics , mathematics , artificial intelligence , machine learning , linguistics , philosophy
The existing methods for analyzing unreplicated fractional factorial experiments that do not contemplate the possibility of outliers in the data have a poor performance for detecting the active effects when that contingency becomes a reality. There are some methods to detect active effects under this experimental setup that consider outliers. We propose a new procedure based on robust regression methods to estimate the effects that allows for outliers. We perform a simulation study to compare its behavior relative to existing methods and find that the new method has a very competitive or even better power. The relative power improves as the contamination and size of outliers increase when the number of active effects is up to four. Copyright © 2012 John Wiley & Sons, Ltd.