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Statistical learning procedures for monitoring regulatory compliance: an application to fisheries data
Author(s) -
LennertCody Cleridy E.,
Berk Richard A.
Publication year - 2007
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2006.00460.x
Subject(s) - categorical variable , suspect , construct (python library) , random forest , fishery , data set , set (abstract data type) , computer science , decision tree , geography , statistics , econometrics , machine learning , artificial intelligence , psychology , mathematics , biology , criminology , programming language
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.