Approximate replication of high-breakdown robust regression techniques

Zeileis, Achim and Kleiber, Christian. (2009) Approximate replication of high-breakdown robust regression techniques. Journal of Economic and Social Measurement, 34 (2-3). pp. 191-203.

[img] PDF - Accepted Version

Official URL: http://edoc.unibas.ch/dok/A5252930

Downloads: Statistics Overview


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.
Faculties and Departments:06 Faculty of Business and Economics > Departement Wirtschaftswissenschaften > Professuren Wirtschaftswissenschaften > ├ľkonometrie und Statistik (Kleiber)
UniBasel Contributors:Kleiber, Christian
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:IOS Press
Note:Publication type according to Uni Basel Research Database: Journal article
Related URLs:
Identification Number:
edoc DOI:
Last Modified:28 Sep 2018 11:23
Deposited On:22 Mar 2012 14:18

Repository Staff Only: item control page