Research Reproducibility as a Survival Analysis

Simple item page
dc.contributor.authorRaff, Edward
dc.date.accessioned2021-05-20T16:16:30Z
dc.date.available2021-05-20T16:16:30Z
dc.date.issued2020-12-17
dc.description.abstractThere has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival analysis problem. We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. The data and code can be found at https://github.com/EdwardRaff/Research-ReproducibilitySurvival-Analysisen_US
dc.description.urihttps://arxiv.org/abs/2012.09932en_US
dc.format.extent13 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m2lchy-zll6
dc.identifier.citationRaff, Edward; Research Reproducibility as a Survival Analysis; Machine Learning (2020); https://arxiv.org/abs/2012.09932en_US
dc.identifier.urihttp://hdl.handle.net/11603/21578
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectmodeling the reproducibility of a paperen_US
dc.subjectsurvival analysis problemen_US
dc.titleResearch Reproducibility as a Survival Analysisen_US
dc.typeTexten_US

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection