The Power of Noise and the Art of Prediction

ZhiMin Xiao and Steve Higgins
Additional contact information
ZhiMin Xiao: University of Exeter

No zu64w, SocArXiv from Center for Open Science

Abstract: Data analysis usually aims to identify a particular signal, such as an intervention effect. Conventional analyses often assume a specific data generation process, which suggests a theoretical model that best fits the data. Machine learning techniques do not make such an assumption. In fact, they encourage multiple models to compete on the same data. Applying logistic regression and machine learning algorithms to real and simulated datasets with different features of noise and signal, we demonstrate that no single model dominates others under all circumstances. By showing when different models shine or struggle, we argue it is both possible and important to conduct comparative analyses.

Date: 2017-08-08
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/5989b681b83f69023ce35051/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:zu64w

DOI: 10.31219/osf.io/zu64w

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().