The Exact Form of the “Ockham Factor” in Model Selection

Jonathan Rougier and Carey E. Priebe

The American Statistician, 2021, vol. 75, issue 3, 288-293

Abstract: We explore the arguments for maximizing the “evidence” as an algorithm for model selection. We show, using a new definition of model complexity which we term “flexibility,” that maximizing the evidence should appeal to both Bayesian and frequentist statisticians. This is due to flexibility’s unique position in the exact decomposition of log-evidence into log-fit minus flexibility. In the Gaussian linear model, flexibility is asymptotically equal to the Bayesian information criterion (BIC) penalty, but we caution against using BIC in place of flexibility for model selection.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2020.1764865 (text/html)
Access to full text is restricted to subscribers.

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:taf:amstat:v:75:y:2021:i:3:p:288-293

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2020.1764865

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().