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Approximate replication of high-breakdown robust regression techniques
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 103123
Author(s) Zeileis, Achim; Kleiber, Christian
Author(s) at UniBasel Kleiber, Christian
Year 2009
Title Approximate replication of high-breakdown robust regression techniques
Journal Journal of Economic and Social Measurement
Volume 34
Number 2-3
Pages / Article-Number 191-203
Keywords Combinatorial optimization, least squares, replication, robust regression, stochastic algorithm
Abstract We present a case study demonstrating that without data and code archives reproducibility is more the exception than the rule, especially if modern, complex algorithms are employed. Specifically, we show that stochastic extensions of OLS, as required in some combinatorial optimization problems arising in high-breakdown robust regression, can be difficult to replicate in the absence of detailed information on tuning parameters and further computational issues.
Publisher IOS Press
ISSN/ISBN 0747-9662
URL http://iospress.metapress.com/content/78321h80552gl442/fulltext.pdf
edoc-URL http://edoc.unibas.ch/dok/A5252930
Full Text on edoc Available
Digital Object Identifier DOI 10.3233/JEM-2009-0314
 
   

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