Approximate replication of high-breakdown robust regression techniques

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.