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BootQC: Bootstrap for Robust Analysis of Aviation Safety Data

In: Developments in Robust Statistics

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  • R. Y. Liu

    (Rutgers University, Department of Statistics)

Abstract

Summary Effective regulation of air traffic and safety requires a comprehensive aviation safety analysis plan. Most of the existing statistical analysis methods prescribed in the current aviation safety databases are parametric in nature, and thus their applicability is much restricted. A nonparametric alternative based on bootstrap methods is pursued in this paper. By assigning proper false alarm rates to the bootstrap control charts proposed in Liu and Tang (1996), a meaningful threshold system is developed for the purpose of regulating and monitoring aviation safety. The threshold system can serve as a set of standards for evaluating the performance of aviation entities, and provide guidelines for identifying unexpected performances and for assigning appropriate corrective measures. Both bootstrap control charts and threshold systems are demonstrated in an analysis of surveillance results collected by the Federal Aviation Administration (FAA) from several air carriers. The demonstration uses the software BootQC developed in Liu and Teng (2000).

Suggested Citation

  • R. Y. Liu, 2003. "BootQC: Bootstrap for Robust Analysis of Aviation Safety Data," Springer Books, in: Rudolf Dutter & Peter Filzmoser & Ursula Gather & Peter J. Rousseeuw (ed.), Developments in Robust Statistics, pages 246-258, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-57338-5_21
    DOI: 10.1007/978-3-642-57338-5_21
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