Statistical learning procedures for monitoring regulatory compliance: an application to fisheries data

Cleridy E. Lennert‐Cody and Richard A. Berk

Journal of the Royal Statistical Society Series A, 2007, vol. 170, issue 3, 671-689

Abstract: Summary. As a special case of statistical learning, ensemble methods are well suited for the analysis of opportunistically collected data that involve many weak and sometimes specialized predictors, especially when subject‐matter knowledge favours inductive approaches. We analyse data on the incidental mortality of dolphins in the purse‐seine fishery for tuna in the eastern Pacific Ocean. The goal is to identify those rare purse‐seine sets for which incidental mortality would be expected but none was reported. The ensemble method random forests is used to classify sets according to whether mortality was (response 1) or was not (response 0) reported. To identify questionable reporting practice, we construct ‘residuals’ as the difference between the categorical response (0,1) and the proportion of trees in the forest that classify a given set as having mortality. Two uses of these residuals to identify suspicious data are illustrated. This approach shows promise as a means of identifying suspect data gathered for environmental monitoring.

Date: 2007
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/j.1467-985X.2006.00460.x

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:bla:jorssa:v:170:y:2007:i:3:p:671-689

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X

Access Statistics for this article

Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples

More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().