Model evaluation, discrepancy function estimation, and social choice theory

Andrew Neath, Joseph Cavanaugh () and Adam Weyhaupt

Computational Statistics, 2015, vol. 30, issue 1, 249 pages

Abstract: A discrepancy function provides for an evaluation of a candidate model by quantifying the disparity between the candidate model and the true model that generated the observed data. The favored model from a candidate class is the one judged to have minimum discrepancy with the true model. The observed data can be regarded as a manifestation of the underlying true model. However, since the data provides only partial information as to the nature of the true model, the selection of a model is a decision that is made in the presence of uncertainty. To characterize this uncertainty, we consider employing resampling to generate multiple manifestations of the true model. Each of the candidate models can then be judged against each of the simulated versions of the true model, resulting in multiple panels of discrepancies. Model evaluation is subsequently achieved by providing an overall judgment on each candidate model. This overall assessment is based on combining the information in the individual discrepancy panels. As social choice theory, or voting theory, addresses the problem of turning individual preferences into a group preference, we see that social choice theory can be used in developing a novel approach to model evaluation. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Akaike information criterion; Conceptual predictive statistic; Model selection; Resampling (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s00180-014-0532-z (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:spr:compst:v:30:y:2015:i:1:p:231-249

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-014-0532-z

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().