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

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  • Cleridy E. Lennert‐Cody
  • Richard A. Berk

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.

Suggested Citation

  • Cleridy E. Lennert‐Cody & Richard A. Berk, 2007. "Statistical learning procedures for monitoring regulatory compliance: an application to fisheries data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 671-689, July.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:3:p:671-689
    DOI: 10.1111/j.1467-985X.2006.00460.x
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    References listed on IDEAS

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    1. Richard A. Berk, 2006. "An Introduction to Ensemble Methods for Data Analysis," Sociological Methods & Research, , vol. 34(3), pages 263-295, February.
    2. Lin, Yi & Jeon, Yongho, 2006. "Random Forests and Adaptive Nearest Neighbors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 578-590, June.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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